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B1127
Title: Understanding changes in brain topology and connectivity through single-subject ICA with empirical population priors Authors:  Amanda Mejia - Indiana University (United States) [presenting]
Abstract: A primary objective in resting-state fMRI studies is the localization of resting-state networks (RSNs), regions of the brain that tend to act in a coordinated way, as well as the functional connectivity between them. These spatial and temporal properties of brain organization may be related to disease progression and treatment, development, and aging, making them of potential scientific and clinical value. A common tool to estimate RSNs is independent component analysis (ICA). However, due to high noise levels in fMRI, population average RSNs are often obtained and form the basis for estimating functional connectivity. Subject-level spatial RSN features are ignored, leading to potential bias in functional connectivity estimates and providing no information on differences in RSN topology. In hierarchical ICA, information shared across subjects is leveraged to improve subject-level estimation, but fitting such models is computationally intensive. We have proposed template ICA, a single-subject ICA model employing empirical population priors, which is computationally efficient and provides accurate subject-level estimates of RSNs and the functional connectivity between then. We will describe how template ICA allows for identification of unique subject-level spatial features, which, along with functional connectivity, can be used to understand better changes in the brain related to disease, aging, and other subject-specific factors.