Abstract

Over the past 25 years, neuroimaging has become a pervasive tool in basic and clinical studies of the human brain around the world. However, despite the millions of people scanned during this era, we still lag far behind the potential for robust bench to bedside translation from this ubiquitous technology. Moreover, the sensitivity of neuroimaging data to normative biological variation in age, sex, and demography, as well as technological variation in platform and methodology, provides additional complexity for generating accurate, replicable, and generalizable neurobiological signatures across the myriad brain-related diseases. Defining normative lifespan population variation (“growth charts”) is critical for both scientific assessment and technical advancement of this framework, and is an essential step in maximizing the potential for clinical translation and individualized medicine. In my talk I will discuss recent work and challenges in aggregating across multiple large neuroimaging datasets to form these growth charts as well as modelling frameworks that may allow individualised assessment based on these charts.