Unsupervised detrending technique using sparse dictionary learning for fMRI preprocessing and analysis

This paper addresses the problem of scanner induced low frequency drift estimation in order to improve the significance of functional magnetic resonance imaging (fMRI) data for statistical analysis. A novel technique is presented to estimate the drift parameters using a sparse general linear model (sGLM) framework. The fMRI signal is modeled as a linear mixture of several signals such as low frequency trend, brain hemodynamic, physiological noise and unexplained signal variations. These signals are considered as underlying sources and sparse dictionary learning (SDL) is used to estimate them. The superior performance of the proposed technique compared to other detrending techniques is illustrated using a simulation study. Furthermore, the proposed technique is validated using real fMRI data, which shows its better capability to estimate drift in presence of spatiotemporal dependencies.