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We propose a multi-hop graph attention module (MGA) that addresses the limitation of CNN in capturing non-local connections of features for predicting brain age. MGA converts feature maps to graphs, calculates distance-based scores, and uses Markov property and graph attention to capture direct and indirect connectivity. Combining MGA with sSE-ResNet18, we achieved a mean absolute error (MAE) of 2.822 years and Pearson\'s correlation coefficient (PCC) of 0.968 using 2,788 T1-weighted MR images of healthy subjects. Our results present a possibility of MGA as a new algorithm for brain age prediction.
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