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There are multiple scales of abstraction from which we can describe the sameimage, depending on whether we are focusing on fine-grained details or a moreglobal attribute of the image. In brain mapping, learning to automaticallyparse images to build representations of both small-scale features (e.g., thepresence of cells or blood vessels) and global properties of an image (e.g.,which brain region the image comes from) is a crucial and open challenge.However, most existing datasets and benchmarks for neuroanatomy consider only asingle downstream task at a time. To bridge this gap, we introduce a newdataset, annotations, and multiple downstream tasks that provide diverse waysto readout information about brain structure and architecture from the sameimage. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric,micrometer-resolution X-ray microtomography images spanning a largethalamocortical section of mouse brain, encompassing multiple cortical andsubcortical regions. We generated a number of different prediction challengesand evaluated several supervised and self-supervised models for brain-regionprediction and pixel-level semantic segmentation of microstructures. Ourexperiments not only highlight the rich heterogeneity of this dataset, but alsoprovide insights into how self-supervised approaches can be used to learnrepresentations that capture multiple attributes of a single image and performwell on a variety of downstream tasks. Datasets, code, and pre-trained baselinemodels are provided at: https://mtneuro.github.io/ .

MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction