… matrices generated from fMRI. The goal of the paper is to extract sparse interpretable
hierarchically-… Xu et al., “Large-scale functional network overlap is a general property of brain …

… ) based on a resting-state fMRI dataset. We further explored four large-scale cognitive networks
which have been widely known: central executive network (CEN), default mode network (…

… connectivity requires solving a formidable large-scale inverse problem from indirect and …
from resting-state functional magnetic resonance data. To this purpose sparse estimation …

… large-scale networks of the brain using resting state fMRI. This … correlation techniques to
four resting-state fMRI scans (each … 2, the short duration connections are dominated by sparse …

… This has lead to the rise of large-scale rest-fMRI data … sparse ℓ 1 regularization 5 for
Support Vector Classification (SVC), and Logistic Regression (Hastie et al., 2009). For non-sparse …

… functional connectivity that links the nodes (Liao et al., 2017). In this review, we focus on
large-scale networks … Dense within relative to sparse between network connections. A modular …

… means of sparse representation with regional rs-fMRI signals… rs-fMRI and non-local, large-scale
brain networks where global and … MRI, we leverage functional MRI during resting state to …

… stroke based on resting-state functional MRI (fMRI). Twenty-… A whole-brain large-scale
functional connectivity network … it may result from the sparse or damaged functional connections. …

… Some methods adopted sparse methods to implement implicit dimension reduction by adding
an extra … The eight selected resting state functional networks included the primary visual …

… Resting-state functional connectivity (RSFC) can be used for mapping large-scale human
brain networks during rest. … Therefore, we propose a multi-task learning based sparse convex …