The multimodal brain (MuMoBrain): exploring the cognitive correlates of connectomes across modalities

Improving our knowledge on the cognitive correlates of connectomes, the anatomical/functional connectivity patterns and their topology in the brain, has been a key aim in recent neuroscience. Indeed, cognitive test performance is strongly related to anatomical and functional connectivity patterns in the brain. These patterns can be measured with several types of macroscopic imaging, such as magnetoencephalography (MEG), resting-state functional magnetic resonance imaging (rsfMRI), and diffusion magnetic resonance imaging (dMRI). Once connectivity is measured, network theory can be used to explore which properties of the brain network most strongly associate with cognition, as has been amply shown within the abovementioned imaging modalities. However, the link between cognition and multimodal imaging characteristics remains largely unknown, even though recent studies suggest that multilayer predictors supersede monolayer correlates of cognitive functioning.

Objectives: To explore the multilayer correlates of cognitive functioning through the framework of ‘multilayer connectomes’, we collected MEG, rsfMRI, and dMRI as well as NPA data from 39 healthy adults (recruited through hersenonderzoek.nl). We will calculate both mono- and multilayer eigenvector centrality (EC) within the fronto-parietal network (FPN), one of the main resting-state networks. These network measures will be used as independent variables in a multiple regression to predict cognitive performance in the domain of executive functioning (EF).

We hypothesize that higher EC within the FPN is a predictor of better EF. Furthermore, we hypothesize that multilayer centrality is a better predictor of EF than monolayer centrality.

Data will be collected during two separate visits:

- Amsterdam UMC - AMC /Spinoza centre for MRS/I and EEG (duration ~3 hours)

- Amsterdam UMC - VUmc for neuropsychological testing and MEG (duration ~3 hours)

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