Data_Sheet_1_Predicting Functional Connectivity From Observed and Latent Structural Connectivity via Eigenvalue Mapping.PDF (1.17 MB)
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Data_Sheet_1_Predicting Functional Connectivity From Observed and Latent Structural Connectivity via Eigenvalue Mapping.PDF

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posted on 15.03.2022, 04:18 authored by Jennifer A. Cummings, Benjamin Sipes, Daniel H. Mathalon, Ashish Raj

Understanding how complex dynamic activity propagates over a static structural network is an overarching question in the field of neuroscience. Previous work has demonstrated that linear graph-theoretic models perform as well as non-linear neural simulations in predicting functional connectivity with the added benefits of low dimensionality and a closed-form solution which make them far less computationally expensive. Here we show a simple model relating the eigenvalues of the structural connectivity and functional networks using the Gamma function, producing a reliable prediction of functional connectivity with a single model parameter. We also investigate the impact of local activity diffusion and long-range interhemispheric connectivity on the structure-function model and show an improvement in functional connectivity prediction when accounting for such latent variables which are often excluded from traditional diffusion tensor imaging (DTI) methods.

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