Dynamics of Brain Activity Captured by Graph Signal Processing of Neuroimaging Data to Predict Human Behaviour

Joint structural and functional modelling of the brain based on multimodal imaging increasingly show potential in elucidating the underpinnings of human cognition. In the graph signal processing (GSP) approach for neuroimaging, brain activity patterns are viewed as graph signals expressed on the structural brain graph built from anatomical connectivity. The energy fraction between functional signals that are in line with structure (termed alignment) and those that are not (liberality), has been linked to behaviour. Here, we examine whether there is also information of interest at the level of temporal fluctuations of alignment and liberality. We consider the prediction of an array of behavioural scores, and show that in many cases, a dynamic characterisation yields additional significant insight.