Abstract
Unravelling how the human brain structure gives rise to function is a central question in neuroscience and remains partially answered. Recent studies show that the graph Laplacian of the human brain’s structural connectivity (SC) plays a dominant role in shaping the pattern of resting-state functional connectivity (FC). The modeling of FC using the graph Laplacian of the brain’s SC is limited, owing to the sparseness of the Laplacian matrix. It is unable to model the negative functional correlations. We extended the graph Laplacian to the hypergraph p-Laplacian in order to describe better the nonlinear and high-order relations between SC and FC. First we estimated those possible links showing negative correlations between the brain areas shared across subjects by statistical analysis. Then we presented a hypergraph p-Laplacian model by embedding the two matrices referring to the sign of the correlations between the brain areas relying on the brain structural connectome. We tested the model on two experimental connectome datasets and evaluated the predicted FC by estimating its Pearson correlation with the empirical FC matrices. The results showed that the proposed diffusion model based on hypergraph p-Laplacian can predict functional correlations more accurately than the models using graph Laplacian as well as hypergraph Laplacian.
Highlights
Humans still know little regarding how the human brain works out cognitive tasks, due to its complex structure [1,2], in which hundreds of thousands of neurons, sets of neural populations and multiple brain regions are interconnected and interact together to yield diverse functions in the brain, both in the absence of external stimuli and performing cognitive tasks
A growing body of research has been performed to explore the relationship between structural connectivity (SC) and functional connectivity (FC) with the help of two different kinds of invasive neuroimaging methods, diffusion magnetic resonance imaging measuring the fiber tracts of the white matter between brain regions [3,4] and functional magnetic resonance imaging recording the blood oxygenation level-dependent (BOLD) signals that characterize ongoing neural activities [5,6]
It can be clearly observed that the FC predicted by the proposed hypergraph p-Laplacian diffusion (HpGD) model was much closer to the empirical FC than the other two models
Summary
Humans still know little regarding how the human brain works out cognitive tasks, due to its complex structure [1,2], in which hundreds of thousands of neurons, sets of neural populations and multiple brain regions are interconnected and interact together to yield diverse functions in the brain, both in the absence of external stimuli and performing cognitive tasks. A number of network metrics, such as node degrees [7,8], shortest path [9], as well as search information and path transitivity [10], etc., have been used to establish the relation between SC and resting-state FC. These models can only partially capture the nature of FC. The proposed hypergraph p-Laplacian diffusion model, termed as the HpGD model, is capable of better capturing the high-order relation between SC and FC, demonstrating better performance than GD and HGD models on simulating FC, including the modeling of the negative correlations.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
