Toward precision medicine in ADHD

Attention-Deficit Hyperactivity Disorder (ADHD) is a complex and heterogeneous neurodevelopmental condition for which curative treatments are lacking. Whilst pharmacological treatments are generally effective and safe, there is considerable inter-individual variability among patients regarding treatment response, required dose, and tolerability. Many of the non-pharmacological treatments, which are preferred to drug-treatment by some patients, either lack efficacy for core symptoms or are associated with small effect sizes. No evidence-based decision tools are currently available to allocate pharmacological or psychosocial treatments based on the patient's clinical, environmental, cognitive, genetic, or biological characteristics. We systematically reviewed potential biomarkers that may help in diagnosing ADHD and/or stratifying ADHD into more homogeneous subgroups and/or predict clinical course, treatment response, and long-term outcome across the lifespan. Most work involved exploratory studies with cognitive, actigraphic and EEG diagnostic markers to predict ADHD, along with relatively few studies exploring markers to subtype ADHD and predict response to treatment. There is a critical need for multisite prospective carefully designed experimentally controlled or observational studies to identify biomarkers that index inter-individual variability and/or predict treatment response

Toward Precision Medicine in ADHD

Attention-Deficit Hyperactivity Disorder (ADHD) is a complex and heterogeneous neurodevelopmental condition for which curative treatments are lacking. Whilst pharmacological treatments are generally effective and safe, there is considerable inter-individual variability among patients regarding treatment response, required dose, and tolerability. Many of the non-pharmacological treatments, which are preferred to drug-treatment by some patients, either lack efficacy for core symptoms or are associated with small effect sizes. No evidence-based decision tools are currently available to allocate pharmacological or psychosocial treatments based on the patient's clinical, environmental, cognitive, genetic, or biological characteristics. We systematically reviewed potential biomarkers that may help in diagnosing ADHD and/or stratifying ADHD into more homogeneous subgroups and/or predict clinical course, treatment response, and long-term outcome across the lifespan. Most work involved exploratory studies with cognitive, actigraphic and EEG diagnostic markers to predict ADHD, along with relatively few studies exploring markers to subtype ADHD and predict response to treatment. There is a critical need for multisite prospective carefully designed experimentally controlled or observational studies to identify biomarkers that index inter-individual variability and/or predict treatment response

Cognitive Based Attention Deficit Hyperactivity Disorder Detection with Ability Assessment Using Auto Encoder Based Hidden Markov Model

Attention deficit hyperactivity disorder (ADHD) is a frequent Neuro-generative mental disorder. It can persist in adulthood and be expressed as a cognitive complaint. Behavioural analysis of ADHD consumes more time. This is a multi-informant complex procedure due to the overlaps in symptomatology which is the cause for delay in diagnosis and treatment. Dur to these behavioural consequences and various causes, no single test is utilized till now for diagnosing this disorder. Hence, a diagnosing model of ADHD based on Continuous Ability Assessment Test (CAAT) can enhance and balance behavioural assessment. The objective behind this study is to use a deep learning based model with CAAT for predicting ADHD. The proposed Auto Encoder Based Hidden Markov Model (AE-HMM) produces low-dimensional features of brain structures, and a novel Pearson Correlation Coefficient (PCC) is employed for normalizing these features in order to minimize batch effects over populations and datasets. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like CogniLearn and 3-D Convolutional Neural Networks (3DCNN). It is found that the proposed AE-HMM method achieves 93.68% of accuracy, 90.66% of sensitivity, 87.72% of specificity, 87.78% of F1-score and 74.22% of kappa score

Transdiagnostic clinical staging for childhood mental health:An adjunctive tool for classifying internalizing and externalizing syndromes that emerge in children aged 5-11 years

Clinical staging is now recognized as a key tool for facilitating innovation in personalized and preventative mental health care. It places a strong emphasis on the salience of indicated prevention, early intervention, and secondary prevention of major mental disorders. By contrast to established models for major mood and psychotic syndromes that emerge after puberty, developments in clinical staging for childhood-onset disorders lags significantly behind. In this article, criteria for a transdiagnostic staging model for those internalizing and externalizing disorders that emerge in childhood is presented. This sits alongside three putative pathophysiological profiles (developmental, circadian, and anxious-arousal) that may underpin these common illness trajectories. Given available evidence, we argue that it is now timely to develop a transdiagnostic staging model for childhood-onset syndromes. It is further argued that a transdiagnostic staging model has the potential to capture more precisely the dimensional, fluctuating developmental patterns of illness progression of childhood psychopathology. Given potential improvements in modelling etiological processes, and delivering more personalized interventions, transdiagnostic clinical staging for childhood holds much promise for assisting to improve outcomes. We finish by presenting an agenda for research in developments of transdiagnostic clinical staging for childhood mental health

Automatic detection of ADHD and ASD from expressive behaviour in RGBD data

Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are neurodevelopmental conditions which impact on a significant number of children and adults. Currently, the diagnosis of such disorders is done by experts who employ standard questionnaires and look for certain behavioural markers through manual observation. Such methods for their diagnosis are not only subjective, difficult to repeat, and costly but also extremely time consuming. In this work, we present a novel methodology to aid diagnostic predictions about the presence/absence of ADHD and ASD by automatic visual analysis of a person's behaviour. To do so, we conduct the questionnaires in a computer-mediated way while recording participants with modern RGBD (Colour+Depth) sensors. In contrast to previous automatic approaches which have focussed only on detecting certain behavioural markers, our approach provides a fully automatic end-to-end system to directly predict ADHD and ASD in adults. Using state of the art facial expression analysis based on Dynamic Deep Learning and 3D analysis of behaviour, we attain classification rates of 96% for Controls vs Condition (ADHD/ASD) groups and 94% for Comorbid (ADHD+ASD) vs ASD only group. We show that our system is a potentially useful time saving contribution to the clinical diagnosis of ADHD and ASD

Developing mHealth remote monitoring technology for attention deficit hyperactivity disorder: a qualitative study eliciting user priorities and needs

Background: Guidelines in the United Kingdom recommend that medication titration for attention deficit hyperactivity disorder (ADHD) should be completed within 4-6 weeks and include regular reviews. However, most clinicians think that weekly clinic contact is infeasible, and audits have shown that this timeline is rarely achieved. Thus, a more effective monitoring and review system is needed; remote monitoring technology (RMT) may be one way to improve current practice. However, little is known about whether patients with ADHD, their families, and clinicians would be interested in using RMT. Objective: To explore patients’, parents’, and health care professionals’ views and attitudes toward using digital technology for remote monitoring during titration for ADHD. Methods: This was a qualitative study, and data were collected through 11 focus groups with adults and young people with ADHD, parents of children with ADHD, and health care professionals (N=59). Results: All participant groups were positive about using RMT in the treatment of ADHD, but they were also aware of barriers to its use, especially around access to technology and integrating RMT into clinical care. They identified that RMT had the most potential for use in the ongoing management and support of ADHD, rather than during the distinct titration period. Participants identified features of RMT that could improve the quality of consultations and support greater self-management. Conclusions: RMT has the potential to augment support and care for ADHD, but it needs to go beyond the titration period and offer more to patients and families than monitoring through outcome measures. Developing and evaluating an mHealth app that incorporates the key features identified by end users is required