Fig. 1.

Simplified illustration of the proposed method in the case of two classes. Using correlation matrices from each class, we extract sets of co-activated sub-networks (Sparse Connectivity Patterns, or SCP) represented by the components of vectors x_k, k = 1, 2. In this simple case, a single SCP (i.e. column vector) is estimated for each class. These SCP’s are fused across classes during estimation depending on the weight parameter λ₂ in Eq. 3. This allows us to extract SCP components that are shared across classes (red sub-network) with an increased analysis power, since the whole dataset is used. This in turns facilitates further identification of differential/class-specific SCP (green and blue sub-networks).