Journal
FRONTIERS IN NEUROSCIENCE
Volume 14, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2020.596109
Keywords
multi-hops connectivity; graph neural network; individual prediction; connectivity-function relationship; fusiform face function
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Funding
- National Key Research and Development Program of China [2017YFB1002504]
- Natural Science Foundation of China [62073260]
- General Program of Natural Science Foundation of Shaanxi Province [2019JM-494]
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In this study, a new method for characterizing brain region function was proposed, leading to significant progress in understanding the relationship between brain connectivity and function. The use of a multi-layer graph neural network showed promise in predicting brain region function.
Brain connectivity plays an important role in determining the brain region's function. Previous researchers proposed that the brain region's function is characterized by that region's input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, this proposal only utilizes direct connectivity profiles and thus is deficient in explaining individual differences in the brain region's function. To overcome this problem, we proposed that a brain region's function is characterized by that region's multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face activation of the right fusiform face area (rFFA) via a multi-layer graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the two-layer graph neural network is the best in characterizing rFFA's face activation and revealed a hierarchical network for the face processing of rFFA.
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