Translation is hampered by insufficient integration of knowledge about different behavior domains such as motor-, language- and social functioning, while these are often interlinked. Both, brain networks and behavior in patients with psychiatric disorders are characterized by disconnection. This disconnection, however, is studied within the modality only, and potential interactions between domains have not been investigated before. A better understanding of these interactions provides context for the validity of pre-clinical and especially animal models of behavior.

The aim of this DC project is to map the interdependence of dysconnectivity across behavioral domains in patients with psychiatric disorders, and to study how this dysconnectivity is related to functional brain network dysconnectivity. This is done by systematically evaluating handling and housing conditions on standard testing procedures and by developing more naturalistic approaches. We are building on a clinical dataset of patients that are studied during clinical treatment in the department of psychiatry, where symptom severity, motor movement, language and social functioning are characterized as part of clinical care. Similarly, EEG recordings are obtained before and after treatment. 

State-of-the-art methods using network theory and machine learning will be used to characterize dysconnectivity within domains, and the links between these domains will be made during the project. The host lab at UMCU is expert in EEG analysis in psychiatric patients and applications of machine learning to behavioral data during treatment. The project looks into the possibility that disconnection in one behavioral domain is associated with disconnection in others (e.g. severity of motor movement disconnection being associated with disconnection in language and social functioning). We test whether successful interventions in one behavior domain can have a positive impact on other domains.