Identifying sex-specific risk architectures for predicting amyloid deposition using neural networks

•WMH is not as important predictor of PiB SUVR as sex, age, and education.•Permuted feature importance is robust for interpreting artificial neural networks.•We develop a novel feature relevance metric that is robust.•Feature relevance identifies a sex-specific risk architecture for predicting PiB. In older adults without dementia, White Matter Hyperintensities (WMH) in MRI have been shown to be highly associated with cerebral amyloid deposition, measured by the Pittsburgh compound B (PiB) PET. However, the relation to age, sex, and education in explaining this association is not well understood. We use the voxel counts of regional WMH, age, one-hot encoded sex, and education to predict the regional PiB using a multilayer perceptron with only rectilinear activations using mean squared error. We then develop a novel, robust metric to understand the relevance of each input variable for prediction. Our observations indicate that sex is the most relevant predictor of PiB and that WMH is not relevant for prediction. These results indicate that there is a sex-specific risk architecture for Aβ deposition.