Modeling the cell-type-specific mesoscale murine connectome with anterograde tracing experiments

The Allen Mouse Brain Connectivity Atlas consists of anterograde tracing experiments targeting diverse structures and classes of projecting neurons. Beyond regional anterograde tracing done in C57BL/6 wild-type mice, a large fraction of experiments are performed using transgenic Cre-lines. This allows access to cell-class-specific whole-brain connectivity information, with class defined by the transgenic lines. However, even though the number of experiments is large, it does not come close to covering all existing cell classes in every area where they exist. Here, we study how much we can fill in these gaps and estimate the cell-class-specific connectivity function given the simplifying assumptions that nearby voxels have smoothly varying projections, but that these projection tensors can change sharply depending on the region and class of the projecting cells. This paper describes the conversion of Cre-line tracer experiments into class-specific connectivity matrices representing the connection strengths between source and target structures. We introduce and validate a novel statistical model for creation of connectivity matrices. We extend the Nadaraya-Watson kernel learning method that we previously used to fill in spatial gaps to also fill in gaps in cell-class connectivity information. To do this, we construct a "cell-class space" based on class-specific averaged regionalized projections and combine smoothing in 3D space as well as in this abstract space to share information between similar neuron classes. Using this method, we construct a set of connectivity matrices using multiple levels of resolution at which discontinuities in connectivity are assumed. We show that the connectivities obtained from this model display expected cell-type- and structure-specific connectivities. We also show that the wild-type connectivity matrix can be factored using a sparse set of factors, and analyze the informativeness of this latent variable model. While standard connectivity studies in mammalian models focus on the overall connection strength between areas, there is an increasing focus on specific connection types between cell classes when describing functions at a local circuit level. Having recently described the importance of such classes in the cortico-thalamic system, we now investigate their importance for estimating brain-wide mesoscopic connectivity. Even within our relatively large dataset, the connectivity data across cell classes is sparse, and so we introduce a method to more reliably extrapolate across classes and estimate connection-type-specific inter-areal connectivity. We observe that this complex connectivity may be described via a relatively small set of factors. While not complete, this connectivity matrix represents a categorical and quantitative improvement in mouse mesoscale connectivity models.