Constrained Learning of Task-Related and Spatially-Coherent Dictionaries from Task fMRI Data

Abstract

Dictionary learning and sparse coding techniques overcome limitations of traditional voxel-level analyses of task-based functional magnetic resonance imaging (fMRI) data by identifying broader temporal and spatial patterns of brain activity. However, prior applications of these methods to task-related fMRI data are not simultaneously optimized to find temporal patterns of activity that change in concert with changes in task conditions and spatial patterns that leverage existing neuroscience knowledge. In this study we present a new sparse dictionary learning method that uses prior knowledge of the temporal pattern of task conditions and the locations of brain regions hypothesized to be involved in the task to decompose fMRI data into temporal patterns of signals that loosely differ between task conditions and sparse spatial patterns that are at least partially similar to known functional network hubs. An efficient on-line optimization framework identifies the temporal and spatial patterns. The method identifies spatial and temporal patterns programmed into synthetic task fMRI data. The proposed method also identifies spatial locations known a priori to be activated by the Attention Network Task (ANT) more completely than competing methods when applied to real fMRI data from 20 healthy young individuals aged 18 to 39 years. Simultaneously leveraging the known temporal structure of the task and biasing solutions towards hypothesized network hubs increases the usefulness of sparse dictionary learning methods applied to task fMRI data.