Nonlinear registration forms a part of standard MRI neuroimaging pipelines but leads to suppression of morphological information. Using attention gates within a convolutional neural network, we explore the effect of the nonlinear registration on age prediction, comparing to linear registration. We show that the network is driven by interpolation effects near the ventricles when trained with nonlinear data, whereas when trained with linear data it considers the whole brain volume. The network may, therefore, be missing cortical changes, limiting the utility of the networks in detecting the early stages of neurological disease.
This abstract and the presentation materials are available to members only; a login is required.