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

摘要

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.

主权项

1. A method for predicting future brain activity of a subject, comprising:
obtaining a previously generated spatio-temporal model representing brain activity of a subject, which model was previously generated from scan data collected with the subject performing a given task; initializing the previously generated spatio-temporal model with current brain activity data derived from a scan of the subject's brain while the subject is performing the same given task; and predicting future brain activity of the subject using the previously generated spatio-temporal model based on the current brain activity data initializing said model, wherein obtaining, initializing, and predicting are performed by a computer.