Modeling Variability in Brain Architecture with Deep Feature Learning

The brain has long been divided into distinct areas based upon its local microstructure, or patterned composition of cells, genes, and proteins. While this taxonomy is incredibly useful and provides an essential roadmap for comparing two brains, there is also immense anatomical variability within areas that must be incorporated into models of brain architecture. In this work we leverage the expressive power of deep neural networks to create a data-driven model of intra- and interbrain area variability. To this end, we train a convolutional neural network that learns relevant microstructural features directly from brain imagery. We then extract features from the network and fit a simple classifier to them, thus creating a simple, robust, and interpretable model of brain architecture. We further propose and show preliminary results for the use of features from deep neural networks in conjunction with unsupervised learning techniques to find fine-grained structure within brain areas. We apply our methods to micron-scale X-ray microtomography images spanning multiple regions in the mouse brain and demonstrate that our deep feature-based model can reliably discriminate between brain areas, is robust to noise, and can be used to reveal anatomically relevant patterns in neural architecture that the network wasn't trained to find.