Sparse Connectivity Components of Brain Connectomes
The human brain processes information via multiple distributed networks. An accurate model of the brain's functional connectome is critical for understanding both normal brain function as well as the dysfunction present in neurologic and neuropsychiatric illnesses. Current methodologies that attempt to discover the organization of the functional connectome, such as ICA, typically assume spatial or temporal separation of the underlying networks. This assumption deviates from an intuitive understanding of brain function, which is that of multiple, inter-dependent spatially overlapping brain networks that efficiently integrate information pertinent to diverse brain functions. It is now increasingly evident that neural systems use parsimonious formations and functional representations to efficiently process information, while minimizing redundancy. This project sparse modeling to develop a methodological framework aiming to understand complex resting-state fMRI connectivity data, but is also applicable to any type of connectome, including structural connectivity matrices. By favoring networks that explain the data via a relatively small number of participating brain regions, we obtain a parsimonious representation of brain function in terms of multiple “Sparse Connectivity Patterns” (SCPs), such that differential presence of these SCPs explains inter-subject variability. In this manner the sparsity-based framework can effectively capture the heterogeneity of functional activity patterns across individuals while potentially highlighting multiple sub-populations within the data that display similar patterns. This approach is described in [1, 2]
Briefly, a connectivity matrix is decomposed into a number of components
which are estimated by solving
Reproducibility of the extracted sparse connectivity components is relatively high, even across studies involving different populations and acquisition protocols:
Philadelphia Neurodevelopmental Cohort HCP + fBIRN
- Eavani, H., et al., Identifying Sparse Connectivity Patterns in the brain using resting-state fMRI. NeuroImage, 2015. 105: p. 286-299
- Eavani, H., et al., Unsupervised learning of functional network dynamics in resting state fMRI, in Information Processing in Medical Imaging (IPMI 2013) 2013: Asilomer, CA.