Approved Research

Cognitive networks in the brain: variability within the population and predictive power for disease

Lay summary

Multiple developmental, psychiatric and neurological conditions (e.g. e.g. dyslexia, intellectual disabilities, schizophrenia, aphasias, dementias) manifest through below-average performance in tasks that tap into different aspects of cognitive functions such as language, memory or attention. These cognitive functions enable individuals to fulfil their full potential and encompass an important dimension for a person's well-being. Thus, characterizing the underlying networks in the brain, and understanding environmental and genetic factors affecting individual variability in these traits has the potential to provide useful risk predictors.

We will first investigate cognitive networks in the imaging subset of the UK Biobank, to characterize how these neurobiological correlates behave in the healthy and atypical brains. To this aim, we will assess structural (grey and white matter) and functional (resting state) MRI measures. We will assess the cortical reorganization that occurs in patients, and whether it has links with the aging processes that occur even in the absence of disease.

Next, we will perform hypothesis-free analyses of relevant brain networks to see whether they are associated with environmental or genetic variables. This will result in the identification of potential environmental risk factors, as well as genome-wide association results. Furthermore, we will perform downstream genetic analyses to further characterize the effects of these brain regions at the genetic level: heritability, genetic correlation, polygenic score analyses.

The validity of these brain correlates as useful proxies to characterize the cognitive networks will be tested through a multi-level analysis: assessing the relationship with the available cognitive tasks in the UK Biobank, and by means of genetic correlations/polygenic score analyses with other datasets.