Exploring Hierarchical Changes in Functional Brain Network Hubs Through Brain-Activity Prediction with Convolutional Neural Networks

Haruka Kawasaki, Satoshi Nishida, Ichiro Kobayashi

Abstract: This study aims to clarify how functional network hubs change during hierarchical visual processing in the human brain through the estimation of brain states from features extracted using a convolutional neural network (CNN), a hierarchical model of image processing. We used representational similarity analysis for brain states predicted through encoding models based on feature representations at each layer of the CNN, and applied the PageRank algorithm to matrices converted from the generated representational dissimilarity matrices to capture the hub characteristics of brain region-related systems. This succeeded in capturing changes in the hubness of interregional brain coordination during hierarchical information processing in the human cerebral cortex in visual processing. Specifically, we found that the hubness of the occipital visual cortex increased in the early phase of visual processing, and that the hubness of the prefrontal cortex and temporal lobe increased in the late phase of visual processing. From the above, we found that our proposed method allows us to capture hierarchical changes in the hubness of interregional coordination.