What is it about?
We developed a method to combine structural and functional connectivity by using an autoregressive model. The autoregressive model is constrained by structural connectivity defining coefficients for Granger causality. The usefulness of the generated effective connections is tested on simulations, ground-truth default mode network experiments, a classification and clustering task. The method can be used for direct and indirect connections, and with raw and deconvoluted BOLD signal.
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Why is it important?
The relationship between structure and function is of interest in many research fields involving the study of complex biological processes. In neuroscience in particular, the fusion of structural and functional data can help to understand the underlying principles of the operational networks in the brain.
Perspectives
This method can lead to new insights into understanding brain effective connections in healthy subjects and in subjects with a neurological disease. Moreover the proposed constrained model is not limited to fMRI and diffusion volumes, but it can also be applied to different domains where structural and time varying data is generated such as two-photon imaging, electroencephalography, and metabolic positron emission tomography.
Alessandro Crimi
Universitat Zurich
Read the Original
This page is a summary of: Structurally Constrained Effective Brain Connectivity, NeuroImage, June 2021, Elsevier,
DOI: 10.1016/j.neuroimage.2021.118288.
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