Human behaviour and mental activities are controlled by goal-directed and anticipatory mechanisms, currently termed Executive Functions (EF). EF disorders are the most common features associated with mental health problems in children. Present classifications of EF disorders (e.g. ADHD) have poor levels of accuracy. No measures or precise sets of observable characteristics are available to date and this situation makes it difficult for clinicians to diagnose these conditions. In previous research, we developed a new clinical method that combines mathematics and artificial intelligence to describe and analyse the EF patterns from thought processes. Notably, we discovered that children with ADHD have characteristic patterns of EF, like a signature of the disorder. This new project is guided by this breakthrough and the hypothesis that mental conditions in children display distinctive EF signatures. We aim to advance our research by standardising, replicating and updating this cutting-edge technology to greatly improve diagnosis in psychiatry.