Unpacking the functional heterogeneity of the Emotional Face Matching Task: a normative modelling approach.

In this study we dissect the heterogeneity that underlies traditional group-level analyses, and determine how individualised patterns of predicted activation relate to age, sex, and variations in acquisition parameters and task design choices. To this end we take advantage of six large open-access/shared datasets and collate a large representative sample of over 7500 participants from which we build a normative of task-evoked activation during a widely used emotional reactivity task, the Emotional Face Matching Task. This enables us to bind heterogeneous datasets to a common reference model and enables meaningful comparisons between them. We then apply this model to the naturalistic and clinically realistic MIND-Set cohort, which is a heterogeneous and highly comorbid sample containing individuals with one or more current diagnosis (affective and anxiety disorders, autism spectrum disorders and/or attention deficit hyperactivity disorder). This enables us to determine whether, and if so how, participants with mental illness and/or neurodivergence differ from the reference cohort, both at the group level and at the level of the individual and in terms of cross-diagnostic symptom domains in addition to diagnosis. We show that patients have, on average, a higher frequency of extreme deviations, and have unique spatial distributions depending on the DSM-IV diagnosis and the number of co-occurring diagnoses when models are constructed using the face>shapes task contrast. Models built using the face>baseline task contrast, have, by comparison, greater predictive value for individuals’ functioning across four transdiagnostic domains. We demonstrate the application of the normative modelling framework to task-based functional neuroimaging data, discuss its potential to further our understanding of individual differences in brain function within reference populations, and further validate the clinical relevance of these models.