Data-Driven Profiles of Attention-Deficit/Hyperactivity Disorder Using Objective and Ecological Measures of Attention, Distractibility and Hyperactivity.

Abstract

In the past two decades, the traditional nosology of Attention-Deficit/Hyperactivity Disorder (ADHD) has been criticized for having insufficient discriminant validity. As an alternative, dimensional frameworks to psychopathology pursue disentangling within-diagnostic heterogeneity and define more reliable and clinically useful nosologies. In line with this trend, in this study, we adopted a data-driven approach to ecological and objective measures of attentional control, impulsivity and hyperactivity, with the aim of identifying clinically useful profiles of ADHD. 110 Spanish-speaking participants (6–16 years) with ADHD (medication-naïve. n = 57) and typically developing (n = 53) completed AULA, a virtual-reality continuous performance test. We first examined AULA performance using DSM-5 diagnosis and found a similar performance profile between ADHD subtypes. Then, we applied hybrid hierarchical k-means clustering algorithms to AULA’s main outcome measures. A five-cluster structure was the most optimal solution. We identified two ADHD phenotypes sharing attention impairments and hyperactivity but with opposing performance profiles on processing speed (PS) and response inhibition; two groups with average and high performance; and one average-performing group with poor sustained attention and slow PS. DSM-5 subtypes cut across cluster profiles. Our findings might suggest that PS and response inhibition, but not attentional processes and gross-motor activity, are useful domains to distinguish between ADHD subpopulations and understand mechanisms underlying attentional impairments. This study highlights the poor feasibility of categorical systems to parse ADHD heterogeneity and the added value of data-driven approaches and VR-based assessments to obtain an objective and less biased characterization of cognitive functioning in individuals with and without ADHD.

Contingency-based flexibility mechanisms through a reinforcement learning model in adults with Attention-Deficit/Hyperactivity Disorder and Obsessive-Compulsive Disorder

“…Another limitation of the present study was the relatively small number of participants to obtain reliable brain conclusions (Turner et al, 2018). Fourth, emerging clustering analysis studies reported various profiles that cut across categorical nosologies in ADHD (Bergwerff et al, 2019;De Nadai et al, 2023;Fernández-Martín et al, 2023). We cannot rule out the existence of OCD and ADHD patterns that might explain these results and, perhaps, the heterogeneous decision-making strategies found in the previous literature (Apergis-Schoute et al, 2023;Hauser et al, 2014;Mazurki et al, 2021;Sethi et al, 2018).…”