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Abstract: The increasing workloads for radiologists in clinical practice lead to the need for an automatic support tool for anomaly detection in brain MRI-scans. While supervised learning methods can detect and localize lesions in brain MRI-scans, the need for large, balanced data sets with pixel-level annotations limits their use. In contrast, unsupervised anomaly detection (UAD) models only require healthy brain data for training. Despite the inherent 3D structure of brain MRI-scans, most UAD studies focus on slicewise processing. In this work, we capture the inter-slice dependencies of the human brain using recurrent neural networks (RNN) and transformer-based self-attention mechanisms together with variational autoencoders (VAE). We show that by this we can improve both reconstruction quality and UAD performance while the number of parameters remain similar to the 2D approach where the slices are processed individually.