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Synaptic networks and Personalized Medicine Approaches to Understand Neurobehavioural Diseases Across the Lifespan

Over a third of the European population suffers from brain diseases, with estimated yearly costs of about 800
billion euros, 35% of Europe?s total disease burden (DiLuca & Olesen, 2014). Synaptic networks are a key
diagnostic and/or therapeutic target in the vast majority of neurobehavioural disorders across the lifespan. The
pathophysiological involvement of various neurotransmitter systems is increasingly recognized, with a central role
for dopamine, glutamate, GABA and serotonin. Evidence from multiple disciplines supports a pivotal role for these
synaptic neurotransmitters both in the disease mechanisms and at least as targets for pharmacological therapy.
However, the knowledge thus far available is still not translated into useful disease biomarkers and personalized
drug therapies. Focusing at the same processes across diseases where the same synaptic networks are affected
in distinct manners, within the scope of a consortium, as is the goal of this call, will provide new perspectives and
cross-fertilizing approaches.
A promising approach for ‘biomarker’ discovery has been based on pattern recognition methods applied to
neuroimaging data, which are already showing potential for clinical application. Neuroimaging/neurophysiology
provide rich multivariate functional and structural information, and helped demonstrating a common brain network
linking brain development, brain ageing, and vulnerability to disease. They are also important tools to associate
individual differences in the human genome, proteome and metabolome to structure and functional variation in brain
diseases. The link between genetic variation and behaviour is indirect and very complex. Because one needs to
consider molecular to network to functional effects, genomic studies require huge sample sizes for determination
of true associations. Focusing on the synapse and neurotransmission and combining neuroimaging methods with
genetic data and intermediate level molecular function information, provided by RNA, protein and metabolite analysis,
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is a more effective and powerful approach to overcome the need of very large sample sizes to link individual disease
susceptibility with structural and functional outcomes. We propose to use neuroimaging, genetic and biochemical
analysis tools and combine hypothesis-driven, data mining and modelling approaches to further understand the
biological disease mechanisms and to identify diagnostic and therapeutic biomarkers of clinical utility.
The consortium is well suited to integrate multimodal approaches across a biological systems level. Multimodal
approaches – genetics, proteomics/metabolomics, neuroimaging, and clinical predictors ? will help explore potential
predictor and mediator/moderator biomarkers for treatment response profiles in autism, schizophrenia and
neurodegenerative disorders. This strategy will improve pathophysiological understanding, and help personalize
treatment selection.