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Network Neuroscience

Olaf Sporns, Editor
2019, Vol. 3, No. 1, Pages 217-236
(doi: 10.1162/netn_a_00066)
© 2018 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
High-resolution data-driven model of the mouse connectome
Article PDF (3.56 MB)
Abstract
Knowledge of mesoscopic brain connectivity is important for understanding inter- and intraregion information processing. Models of structural connectivity are typically constructed and analyzed with the assumption that regions are homogeneous. We instead use the Allen Mouse Brain Connectivity Atlas to construct a model of whole-brain connectivity at the scale of 100 μm voxels. The data consist of 428 anterograde tracing experiments in wild type C57BL/6J mice, mapping fluorescently labeled neuronal projections brain-wide. Inferring spatial connectivity with this dataset is underdetermined, since the approximately 2 × 105 source voxels outnumber the number of experiments. To address this issue, we assume that connection patterns and strengths vary smoothly across major brain divisions. We model the connectivity at each voxel as a radial basis kernel-weighted average of the projection patterns of nearby injections. The voxel model outperforms a previous regional model in predicting held-out experiments and compared with a human-curated dataset. This voxel-scale model of the mouse connectome permits researchers to extend their previous analyses of structural connectivity to much higher levels of resolution, and it allows for comparison with functional imaging and other datasets.Anatomical tracing experiments can provide a wealth of information regarding connectivities originating from the injection sites. However, it is difficult to integrate all this information into a comprehensive connectivity model. In this study we construct a high-resolution model of the mouse brain connectome using the assumption that connectivity patterns vary smoothly within brain regions, and we present several extensions of this model. We believe that this higher resolution connectome will be of great use to the community, enabling comparisons with other data modalities, such as functional imaging and gene expression, as well as for theoretical studies.