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.
Highlights
Brain network structure, across many spatial scales, plays an important role in facilitating and constraining neural computations
Facilitated by new tracing techniques, image-processing algorithms, and high-throughput methods, mesoscale data with partial to full brain coverage exist in animals such as the fly (Jenett et al, 2012; Shih et al, 2015) and mouse (Gamanut et al, 2018; Oh et al, 2014; Zingg et al, 2014), and such data are being collected from other model organisms such as rat (Bota, Dong, & Swanson, 2003) and marmoset (Majka et al, 2016)
We find the logarithmically transformed weights are best fit by a multiple component Gaussian mixture model (GMM)
Summary
Across many spatial scales, plays an important role in facilitating and constraining neural computations. 2011; Glickfeld, Andermann, Bonin, & Reid, 2013; Kleinfeld et al, 2011; White, Southgate, Thomson, & Brenner, 1986) or a coarse description of connectivity between larger regions (Felleman & Van Essen, 1991; Sporns, 2010). In between these two extremes is mesoscopic structural connectivity: a coarser scale than that of single neurons or cortical columns but finer than whole-brain regions (Bohland et al, 2009). Facilitated by new tracing techniques, image-processing algorithms, and high-throughput methods, mesoscale data with partial to full brain coverage exist in animals such as the fly (Jenett et al, 2012; Shih et al, 2015) and mouse (Gamanut et al, 2018; Oh et al, 2014; Zingg et al, 2014), and such data are being collected from other model organisms such as rat (Bota, Dong, & Swanson, 2003) and marmoset (Majka et al, 2016)
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