Discovering network-level functional interactions from working memory fMRI data

It is widely believed that working memory process involves large-scale functional interactions among multiple brain networks. However, network-level functional interactions across large-scale brain networks in working memory have been rarely explored yet in the literature. In this paper, we propose a novel framework for modeling network-level functional interactions in working memory based on our publicly released 358 DICCCOL landmarks. First, 14 DICCCOLs are detected as group-wise activated ROIs via GLM and compose the `basic network´ of working memory. Second, the time-frequency functional interaction patterns of each pair of activated DICCCOL and other DICCCOLs are calculated using cross-wavelet transform. Third, the common functional interaction patterns and corresponding brain networks are learned via effective online dictionary learning and sparse coding methods. Experimental results showed that multiple brain networks are involved in working memory processes. More importantly, each brain network interacts with the `basic network´ via a specific functionally meaningful time-frequency interaction pattern.