Kernel-Regularized ICA for Computing Functional Topography from Resting-State fMRI
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Topographic regularity is a fundamental property in brain connectivity. In this work, we present a novel method for studying topographic regularity of functional connectivity with resting-state fMRI (rfMRI). Our main idea is to incorporate topographically regular structural connectivity in independent component analysis (ICA), and our method is motivated by the recent development of novel tractography and fiber filtering algorithms that can generate highly organized fiber bundles connecting different brain regions. By leveraging these cutting-edge fiber tracking and filtering algorithms, here we develop a novel kernel-regularized ICA method for extracting functional topography with rfMRI signals. In our experiments, we use rfMRI scans of 35 unrelated, right-handed subjects from the Human Connectome Project (HCP) to study the functional topography of the motor cortex. We first demonstrate that our method can generate functional connectivity maps with more regular topography than conventional group ICA. We also show that the components extracted by our algorithm are able to capture co-activation patterns that represent the organized topography of the motor cortex across the hemispheres. Finally, we show that our method achieves improved reproducibility as compared to conventional group ICA.
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