Precision Psychiatry for Compulsivity

Psychiatric disorders are a leading cause of morbidity and mortality worldwide. Among those, obsessive-compulsive disorder (OCD) is the 4th most common psychiatric condition, affecting more than 2.5 million people worldwide. In recent WHO reports, OCD, together with related disorders, is listed as the 6th largest contribution of health loss globally and appears in the top 10 causes of years lost to disability. Unfortunately, even though a substantial body of work has documented cognitive and biological factors that contribute to this debilitating disease, translation to clinical settings has significantly stalled. One barrier is that we don't have models of cognition and mental illness that can be used in individual patients. Additionally, we tend to get measurements only at single timepoints, which provide only a snapshot of the individual's current state and limited understanding of whether observed effects are stable over time or state dependent. This is a major limitation as psychiatric disorders show within-subject large symptomatic fluctuations over days/months and when intervening therapeutically. This project leverages new approaches to shed light on mechanisms of and potential targets for therapeutic intervention at the subject level. Much like a cough can have many different causes, OCD can result from a variety of different sources. For example, OCD patients can show increased propensity to form repetitive behaviours (e.g., habitual hand washing), difficulties in shifting attention away from patterns of thoughts (e.g., thinking that something bad will happen) or problems in dealing with uncertain information (e.g., chances of getting germs if touching a door handle). My research aims at clarifying which cognitive process might be more relevant for a given patient to enable tailored intervention. To this aim, I will use mathematical models to build the equivalent of the so-called "growth charts". These are a cornerstone of pediatric healthcare and are routinely used to identify whether a child height or weight is on the expected trajectory with respect to a reference population. Similarly, by building charts for different cognitive domains, I aim at providing tools to identify whether a person is on the expected trajectory enabling detection of individuals with high atypicality and prediction in terms of treatment response. Because repetitive behaviours and rigid patterns of thinking become habitual over a prolonged period, my research will also investigate how neural circuits change while forming these inflexible patterns of behavior and thinking. Recently, using neuroimaging techniques, I found that group averages are not representative of single subject network organization, which might carry idiosyncratic information. Therefore, I will use the same neuroimaging approach, centered on a highly sampled and longitudinal methodology, to measure use-driven plasticity in each individual and identify how neural circuits change over the time course of weeks/months. Two different individuals with OCD may both be sick but for very different reasons. Identifying the cognitive mechanism most relevant for each patient can be used to indicate that a certain treatment is likely to be most effective. For example, a combination of a medication and a specific cognitive behavioural therapy may be effective in people with a certain set of scores reflecting alterations of specific neural circuits. A clinician could then use that information, in combination with their expert clinical evaluation, to make a better treatment decision. In this way, the project aims to establish the evidence base for the efficacy of individually tailored approaches and provide clinicians with data grounded in biology to improve treatment of OCD and related disorders. By investigating use-driven brain plasticity, this research also aims at identifying mechanisms and potential targets of therapies aimed at inducing brain and behavioural changes.