Multimodal Longitudinal and Machine Learning-based Predictive Modelling to Understand the Development of Eating Disorders
Multimodal Longitudinal and Machine Learning-based Predictive Modelling to Understand the Development of Eating Disorders
The aim of this observational study is to elucidate the biopsychosocial (including neural, psychological, and social) basis of eating disorders (EDs).
The investigators will use functional and structural neuroimaging, psychological as well as environmental data to identify both shared and distinct behavioural/neural processes across ED diagnoses. The investigators will use advanced statistical methods such as machine learning based models.
The investigators will carry out analysis on the data already collected in the STRATIFY (Brain network based stratification of reinforcement-related disorders, IRAS ID 218030) and IMAGEN studies (Reinforcement-related behaviour in normal brain function and psychopathology, reference PNM/10/11-126), including participants with Anorexia Nervosa (N=60), Bulimia Nervosa (N=52), Binge eating disorder (N=27) and healthy controls. In addition, the investigators will recruit 30 new participants with a binge eating disorder using the original STRATIFY study protocol to enlarge the binge eating disorder group, so that its sample size is comparable to the other groups.
Participants will complete online questionnaires, take an online clinical interview, and undergo a research visit, including brain scans, collection of blood and urine samples, and assessment using a range of cognitive and behavioural measures.
Eating disorders (EDs) are serious mental illnesses that involve a range of disturbed emotions, cognitions, and behaviours related to body shape/weight and eating. The causes of EDs are complex and involve many biological, psychological, and social factors. The investigators are interested in understanding the connections between a range of biological, psychological, social factors and eating disorders. This will help us understand the basis of diagnostic classifications, which will promote early intervention and the identification of new areas to target in treatments.
The investigators will analyse the data already collected in the STRATIFY (Brain network based stratification of reinforcement-related disorders, IRAS ID 218030) and IMAGEN studies (Reinforcement-related behaviour in normal brain function and psychopathology, reference PNM/10/11-126), including patients with Anorexia Nervosa (N=60), Bulimia Nervosa (N=52), Binge eating disorder (N=27) and healthy controls. In addition, the investigators will recruit 30 new participants with a current binge eating disorder using the original STRATIFY protocol to enlarge the binge eating disorder group, so that its sample size is comparable to the other groups. Due to funding limitations, 18 participants will take part in the full assessments, including the research visit (MRI scans, blood, and urine samples). The other 12 participants will take part in the online parts of the assessments only, without the research visit.
The investigators will use neuroimaging, cognitive, psychological and life events data to assess if behavioural/neural processes differentiate one eating disorder from another and if there are similar processes across ED diagnoses. The investigators will use advanced statistical methods such as machine learning based models.
The investigators will further test whether the identified behavioural and neurological processes can predict future disease risk, by using data from the IMAGEN study - a longitudinal population-based genetic and imaging study - that involves over 2000 participants followed up from adolescence to early adulthood.
Inclusion Criteria:
Exclusion Criteria: