Towards and ecological characterization of affective and sleep comorbidities in ADHD through temporal network analysis.

Attention Deficit Hyperactivity Disorder (ADHD) is the most prevalent neurodevelopmental disorder, affecting 2.5-5% of adolescents. The core clinical difficulties observed in ADHD include inattention, hyperactivity and impulsivity. However, individuals with ADHD also suffer from high rates of emotional and sleep problems, which significantly affect functional impairment, particularly during adolescence. Until now, ADHD, emotional and sleep problems have been mostly considered as separate entities, which have been assessed retrospectively through questionnaires and clinical interviews. These classical approaches present significant limitations. In particular, retrospective assessments provide at best a “static” picture, that cannot capture how different symptoms fluctuate and interact in daily life. As a consequence, our understanding of dynamic interactions linking ADHD to emotional and sleep difficulties remains limited, strongly constraining our ability to personalize clinical care.

In the present project we will combine, for the first time, novel digital phenotyping techniques with advanced computational analyses, in order to address current limitations in behavioral assessments of ADHD. We will leverage technological advances in wearable devices, including portable polysomnography, and smartphone-based neurocognitive and clinical assessments. Digital phenotyping data will be collected over a period of two weeks, in adolescents with ADHD and age-matched healthy controls. This approach will provide a high-resolution dynamic characterization of multiple clinical domains, as they unfold in daily life in, relation to environmental factors. Digital phenotyping will be combined with gold-standard clinical and neurocognitive assessments, which will be repeated at a 6-month longitudinal follow-up. We will employ advanced computational analysis techniques to dissect networks of dynamic interactions between symptoms contributing to affective and sleep comorbidities. We hypothesize that differences in how symptoms interact in daily life will predict differential response to treatment, opening opportunities for direct clinical translation. The goal of this project is to help pave the way towards a more personalized and holistic approach to clinical management of ADHD.