Connecting cross-condition patterns of brain connectivity towards a common mechanism of mental conditions and prediction connectomics
The brain is one of the most complex living systems we know and has an enormous capacity to regulate our physiology, behaviour and cognition. 30% of the European population however has to deal with a mental challenge, ranging from depression to burnout to psychosis, etcetera. These conditions are traditionally seen as separate disorders, but there is growing evidence that many mental conditions share overlap in terms of their genetics and symptomatology. The brain mechanisms behind this cross-disorder overlap reflecting a common biological factor of mental conditions remains unknown. One of the key problems is that the current field is centralised around ‘single-condition examinations’, lacking specificity and selectivity of macroscale mechanisms, leaving us blind for which brain attributes play a common versus a unique role across and within mental conditions. The goal of CONNECT is to find an underlying shared biological mechanism of mental conditions: I hypothesise that the organizational principles of the healthy brain network form a common network system for shaping relationships across disorders. With CONNECT I want to map the total brain space of cross-disease relationships to disentangle shared and specific mechanisms of cognitive function and disease disfunction. I want to build (WP0) a large multi-disorder MRI database to compare (WP1) brain fingerprints across a wide range of conditions. I will (WP2) develop a mechanistic framework to fundamentally describe cross- condition interactions and model the shared mechanisms of involvement of brain networks in brain function. This model will be leveraged into (WP2/3) a comprehensive connection catalog that systematically maps for all circuitry their common vs unique role in cognitive functions and their subsequent involvement in the spectrum of mental conditions. Disentangling disease-common from disease-specific effects, I will use Machine Learning to pave the way for (WP4) ‘prediction connectomics’.