▶ Multimodal connectome
Connectivity analysis is one of the most representative methods for quantifying MRI data by measuring the interconnected strengths among brain regions or networks. Structural connectivity is estimated via diffusion MRI tractography, and functional connectivity is measured using inter-regional time series correlation. As different imaging modalities provide different information, the investigation of structure-function coupling is one of the crucial research interests.
▶ Connectome manifold (i.e., gradient)
Manifold learning techniques have gained significant traction to study large-scale principles of neuroimaging data, owing to its advantage of representing complex connectivity patterns in a compact analytical space. The main idea is to project connectome data into low dimensional spaces to capture principal dimensions of the whole-brain organization. Offering a novel and continuous reference frame to study macroscale connectivity, these techniques can effectively visualize the principles of cortical organization.
