Translating single-neuron axonal reconstructions into meso-scale connectivity statistics in the mouse somatosensory thalamus.

Characterizing the connectomic and morphological diversity of thalamic neurons is key for better understanding how the thalamus relays sensory inputs to the cortex. The recent public release of complete single-neuron morphological reconstructions enables the analysis of previously inaccessible connectivity patterns from individual neurons. Here we focus on the Ventral Posteromedial (VPM) nucleus and characterize the full diversity of 257 VPM neurons, obtained by combining data from the MouseLight and Braintell projects.

Analyzing Thalamocortical Tract-Tracing Experiments in a Common Reference Space.

Current mesoscale connectivity atlases provide limited information about the organization of thalamocortical projections in the mouse brain. Labeling the projections of spatially restricted neuron populations in thalamus can provide a functionally relevant level of connectomic analysis, but these need to be integrated within the same common reference space. Here, we present a pipeline for the segmentation, registration, integration and analysis of multiple tract-tracing experiments.

Progress towards a cellularly resolved mouse mesoconnectome is empowered by data fusion and new neuroanatomy techniques.

Over the past decade there has been a rapid improvement in techniques for obtaining large-scale cellular level data related to the mouse brain connectome. However, a detailed mapping of cell-type-specific projection patterns is lacking, which would, for instance, allow us to study the role of circuit motifs in cognitive processes. In this work, we review advanced neuroanatomical and data fusion techniques within the context of a proposed Multimodal Connectomic Integration Framework for augmenting the cellularly resolved mouse mesoconnectome.

Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain.

Finding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent sources of information that link both modalities at the voxel level. We performed separate analyses on sets of projections from the visual cortex, the caudoputamen and the midbrain reticular nucleus, and we determined those brain areas, injections and genes that were most involved in independent components that link both gene expression and projection density data, while we validated their biological context through enrichment analysis.

Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data.

Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes.