1. A method, comprising: obtaining a raw spatio-temporal dataset acquired from scanning a brain of a subject performing a given task; constructing a full spatio-temporal model using the raw spatio-temporal dataset, wherein the full spatio-temporal model represents brain activity that occurs in all regions of the subject's brain in response to the subject performing the given task; selecting model parameters from the full spatio-temporal model which meet or exceed a threshold parameter that defines a level of causal relation between voxels in the obtained dataset, wherein the threshold parameter is based on a prediction power threshold value that specifies a threshold for which brain activity in a given voxel predicts brain activity in another voxel; generating a reduced model representation of the full spatio-temporal model using the selected model parameters; storing the reduced model of the full spatio-temporal model in a data storage system; and utilizing the reduced model of the full spatio-temporal model to predict future brain activity of the subject by performing a process comprising: accessing the reduced model of the full spatio-temporal model from the data storage system; obtaining scan data representing a current estimate of brain activity of the subject performing the given task at an initial time T 0 ; utilizing the obtained scan data to initialize the reduced model of the spatio-temporal model with the current estimate of the brain activity at the initial time T 0 ; and utilizing model parameters of the initialized reduced model of the spatio-temporal model to predict future brain activity of the subject at one or more times subsequent to the initial time To without having to utilize scan data of current activity of the subject for the one or more times subsequent to the initial time T 0 ; wherein the method is performed by a computer executing program code.