Molecular precision mental health
Through a multidisciplinary approach, we are dedicated to developing the next-generation markers in mental health that will enable the implementation of measurement-based treatments.
In substantially transforming the clinical approach toward mental health conditions, we aim to provide accessible and precise diagnostic tools that will enhance advice for consumers and help guide clinical research toward new therapeutics.
Our comprehension of the molecular drivers across mental health disorders remains elusive. We seek to improve upon traditional classification systems and explore novel insights into the mechanisms of metabolic dysregulation strongly associated with mental illness. However, there are a lack of complex data sets, in parallel, with suitable cohorts of patients and appropriate tissue samples to address these questions.
We have established researchers from diverse fields to help facilitate access and analysis of a broad spectrum of biological materials, which have been sourced from sizeable cohorts of healthy volunteers, as well as experimental and well-characterised psychiatric patients across various mental health conditions.
Our node will provide an innovative approach to the assessed samples, including paralleled access to the rare availability of cerebrospinal fluid matched against peripheral fluids from the same participants.
Within neuropsychiatric conditions, an antiquated classification system reliant on syndromic features ignores the diverse neurobiological and molecular processes underlying mental health conditions.
In redefining psychiatric conditions to incorporate a molecular approach, we can greatly improve our understanding of shared pathophysiological processes underlying different mental illnesses.
Our approach can also reveal new connections between metabolic diseases and mental health issues, aiding in the development of prospective fluid biomarker sets that reflect the causal factors in mental health disorders. In turn, improving our molecular understanding through integration of complex data may also help to understand metabolic diseases themselves.