No 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.

In the past two decades, the traditional nosology of attention-deficit/hyperactivity disorder (ADHD) has been criticized for having insufficient discriminant validity. In line with current trends, in the present study, we combined a data-driven approach with the advantages of virtual reality aiming to identify novel behavioral profiles of ADHD based on ecological and performance-based measures of inattention, impulsivity, and hyperactivity. One hundred and ten Spanish-speaking participants (6–16 years) with ADHD (medication-naïve, n = 57) and typically developing participants (n = 53) completed AULA, a continuous performance test embedded in virtual reality. We performed hybrid hierarchical k-means clustering methods over the whole sample on the normalized t-scores of AULA main indices. A five-cluster structure was the most optimal solution. We did not replicate ADHD subtypes. Instead, we identified two clusters sharing clinical scores on attention indices, susceptibility to distraction, and head motor activity, but with opposing scores on mean reaction time and commission errors; two clusters with good performance; and one cluster with average scores but increased response variability and slow RT. DSM-5 subtypes cut across cluster profiles. Our results suggest that latency of response and response inhibition could serve to distinguish among ADHD subpopulations and guide neuropsychological interventions. Motor activity, in contrast, seems to be a common feature among ADHD subgroups. 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 accurate characterization of cognitive functioning in individuals with and without ADHD.

No abstract

In the past two decades, the traditional subtypes of Attention-Deficit/Hyperactivity Disorder (ADHD) have been criticized for having substantial variability in symptom manifestation, clinical course, and treatment response. In the present study, we questioned whether an objective and ecological assessment of attentional control, impulsivity, and hyperactivity, the core symptom domains on which ADHD diagnosis is currently based, could yield similar phenotypic profiles to those defined by DSM-5 criteria. 110 Spanish-speaking children and adolescents (6–16 years) with ADHD (n = 57) and typically developing (n = 53) completed AULA, a continuous performance test embedded in virtual reality. We found that ADHD-Combined and ADHD-Inattentive subtypes exhibited the same performance profile. Then, we applied hybrid hierarchical k-means clustering algorithms to AULA’s main outcome measures. A five-cluster structure was the most optimal solution based on several validation indices. We identified two ADHD phenotypes sharing attention impairments and hyperactivity but with an opposing performance profile on processing speed (PS) and response inhibition domains; two normative groups with average and high performance; and one profile with relatively intact performance but 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. This study highlights the poor feasibility of traditional categorical systems to parse ADHD heterogeneity and the added value of VR-based neuropsychological assessment to obtain an objective and less biased characterization of cognitive functioning in individuals with and without ADHD.