SYSTEMS AND METHODS FOR MODELING AND PROCESSING FUNCTIONAL MAGNETIC RESONANCE IMAGE DATA USING FULL-BRAIN VECTOR AUTO-REGRESSIVE MODEL

Systems and methods for modeling functional magnetic resonance image datasets using a multivariate auto-regressive model which captures temporal dynamics in the data, and creates a reduced representation of the dataset representative of functional connectivity of voxels with respect to brain activity. Raw spatio-temporal data is processed using a multivariate auto-regressive model, wherein coefficients in the model with high weights are retained as indices that best describe the full spatio-temporal data. When there are a relatively small number of temporal samples of the data, sparse regression techniques are used to build the model. The model coefficients are used to perform data processing functions such as indexing, prediction, and classification.