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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Graph propagation network captures individual connectivity-function relationship through predicting functional

Dongya Wu, Xin Li

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Understanding brain function requires mapping connectivity. This study shows multi-hop structural connectivity, not just direct links, significantly improves predicting individual brain activity, advancing neuroscience.

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    Area of Science:

    • Neuroscience
    • Computational Neuroscience
    • Network Science

    Background:

    • Brain function is intrinsically linked to its underlying structural and functional connectivity.
    • Previous work utilized functional connectivity to predict individual brain activity, but structural connectivity remains a challenge.
    • Establishing a robust individual connectivity-function relationship is crucial for understanding brain organization.

    Purpose of the Study:

    • To enhance the prediction of individual functional brain activation using structural connectivity.
    • To investigate the utility of multi-hop structural connectivity pathways over direct connections.
    • To identify key intermediate brain regions involved in predicting functional activation.

    Main Methods:

    • Development and application of a graph propagation network model.
    • Incorporation of multi-hop structural connectivity features into the model.
    • Quantitative assessment of prediction accuracy for individual functional activations.

    Main Results:

    • Multi-hop structural connectivity improved the prediction of individual functional activations by over 90%.
    • Identification of specific intermediate brain regions contributing to accurate functional activation prediction.
    • Demonstration of the efficacy of graph propagation networks in capturing complex connectivity patterns.

    Conclusions:

    • Multi-hop structural connectivity is a superior predictor of individual functional brain activation compared to direct connectivity.
    • The findings offer insights into how brain function emerges from the network's structural architecture.
    • This research advances the development of the connectivity-function relationship in neuroscience.