Who we are
In mental health, it’s not that we lack effective treatments, it’s that certain treatments will only work for certain people. Without tools that can tell us what treatment is right for what person, mental health practitioners are forced to prescribe a first-line treatment and adopt a wait-and-see approach. Patients who do not benefit after several weeks of treatment are often switched to a new treatment and clinicians must wait and see again. The associated individual and societal burden are enormous.
A key goal for modern mental health research is to improve how we prescribe mental health treatments. We know that mental health conditions arise from the interaction of many genes, each of which interacts strongly with environmental factors, such as early adversity, stress, nutrition, social support and so on. This means that even in patients who show extremely similar symptoms, mental health practitioners are often dealing with different underlying pathologies that are destined to respond to different treatment approaches. Unfortunately, science has not reached a point where the vast majority of this complexity can be leveraged to the patient’s benefit. But, we (The Gillan Lab, with the support of our funders, MQ Mental Health) are striving to make progress with this through a large-scale observational study and the application of predictive machine learning.
This research requires participation from thousands of patients who have just started a course of CBT. To collect a sample of this size, it would normally take years using a traditional in-person research methodology that would involve bringing participants into the lab on a number of occasions. This is both times consuming and costly for participants and researchers and it’s clear that the mental health community at large simply cannot wait. To address this, we have developed an innovative Internet-based methodology that allows people to participate from the comfort of their own homes from any part of the world.
If successful, this tool could be used by patients via a mobile device either at home or as they sit in the waiting room, before being seen by their doctor; thereby improving both the accuracy and speed with which doctors can make treatment decisions.
Claire’s lab at Trinity College Dublin is interested in developing novel approaches to studying brain health in psychiatric and aging populations – a key goal is to develop objective tests that can be used to diagnose individuals and predict who will respond to which treatment. Claire is perhaps most well-known for her work in the area of goal-directed learning and habits in obsessive-compulsive disorder (w/ Prof Trevor Robbins @ Cambridge); research that earned her the Junior Investigator Award from British Association for Psychopharmacology in 2015 and a Sir Henry Wellcome Postdoctoral Fellowship. More recently, she has focused on how individual differences in cognition can leave individuals susceptible to mental illness and how those propensities manifest trans-diagnostically (w/ Profs Nathaniel Daw, Liz Phelps @ NYU). Her lab at Trinity was founded in 2017 and is building on these areas of research, linking dimensional psychiatric markers to neurophysiology, developing new models of disease and treatment response, and following individuals over time to study and ultimately model the trajectory of mental illness. Claire’s work is supported by a fellowship from MQ and grant funding from the Wellcome Trust and Global Brain Health Institute. She serves on the editorial board for Brain and Neurosciences Advances, the new flagship journal of the British Neuroscience Association.
Kevin holds a BA in Marketing with a focus on digital technologies, A Higher Diploma in Psychology and a Master’s in Applied Psychology. His research to date has mainly focused on using technology to facilitate psychological interventions and create lasting behavioural change.
At Trinity College Dublin, Kevin is a Research Assistant where he is combining his Marketing and Psychology background to assist in the management of an MQ-funded project aimed at developing an algorithmic tool to help clinicians make more personalised treatment decisions for mental health patients – using web-based data collection, cognitive tests, computational modeling, and machine learning.