Embracing complexity in the characterisation and trcking of individual wellbeing across development

"Neurodevelopmental disorders (NDDs) including ADHD, autism, developmental language disorder and specific learning disabilities are among the most frequently diagnosed paediatric conditions. These NDDs may often have an adverse, long-term impact for the individuals affected and the societies in which they live. NDDs are currently assessed through a variety of educational, health and medical service pathways, each of which typically applies a different evidence base and associated measures to derive categorical diagnoses. However, converging research evidence shows that this conventional approach inadequately captures either individual variation or overlaps between NDDs in their underlying symptom dimensions (genes, brains, cognition).

This project will build upon the emerging research domain criterion framework, to measure and characterise the complexity of the cognitive, affective and emotional processing dimensions that underpin broadly sampled NDDs, in both children and young adults. The unique combination of leading-edge analytic methods and innovative protocols for sampling and data collection (incl. remote testing), will be used to exploit both primary and secondary research data to enhance diagnostic sensitivity to detect, track, predict, and evaluate NDD clusters. Based on published transdiagnostic protocols and new analyses of outcomes of data, sampled across a range of NDDs, a set of assessment measures will be developed and compiled to assess key cognitive and mental health domains. New data using the assessment batteries created will subsequently be used to obtain data from transdiagnostic samples of children and adults who have been referred to assessment and support services for suspected or previously diagnosed difficulties. Toward the objective to study neurodevelopment and disorders from a broader perspective, the project will explore and validate a range of bespoke pipelines for data analyses, including latent variable and cluster-based approaches and dynamic network modelling. These techniques are, a priori, well-suited for the data-driven, dimensional frameworks proposed, and afford the potential to characterise complexity in continuous, multivariate data, beyond that offered by more traditional case-control designs.
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