Journal: Nature Computational Science

Article Title: A simulated annealing algorithm for randomizing weighted networks

doi: 10.1038/s43588-024-00735-z

Figure Lengend Snippet: Top: wiring diagrams for the Drosophila , mouse, rat and macaque connectomes. Center and bottom: scatter plots of strengths of the empirical (abscissa) and simulated annealing-derived networks (ordinate) for all 10,000 nulls, where each point represents a brain region, for in-strengths (center row) and out-strengths (bottom row). Marginal distribution histograms are shown on the top and right axes. The mean and standard deviation across 10,000 Spearman rank-order correlation coefficients are given in the top left of each plot. The linear regression lines (blue) are computed over the whole ensemble for visualization purposes. The identity line (black) is provided as reference.

Article Snippet: The mouse connectome was reconstructed using publicly available data from 461 tract-tracing experiments conducted in wild-type mice by the Allen Institute for Brain Science , .

Techniques: Derivative Assay, Standard Deviation

Journal: bioRxiv

Article Title: Directionality bias is necessary to explain spatiotemporal progression of pathology in mouse models of tauopathy

doi: 10.1101/2024.06.04.597478

Figure Lengend Snippet: A. & B. Schematics showing the etiology of directional spread bias at the microscopic and network levels. Inside of axons, pathological tau can migrate by passive diffusion or through energy-dependent directed transport either in the anterograde (parallel to axon polarity) or retrograde (antiparallel to axon polarity) directions ( A ). At a network level, this manifests as an directionally biased flow along the directed connectome B. By convention, c ij indicates a connection originating in region i and terminating in region j , therefore a net flow from i to j along c ij would be considered to be anterograde-biased . C. The Allen Mouse Brain Connectome Atlas (AMBCA) ) visualized as a heatmap. D. Scatterplots showing the associations between the regional end-timepoint (9 MPI) pathology in the IbaStrInj ) experiment (see ) and the average connectivity from ( left ) and ( right ) seeded regions CP and MOp. Tau shows a highly significant association with incoming but not outgoing connectivity. E. Violin plots showing the associations between tau pathology across all studies and time points and three pairs of region-level graph metrics; from right to left: outgoing and incoming connectivity to seed, out- and in-degree, and the first eigenvectors ( v 1 ) of L ret and L ant (see Materials and Methods ). One-sample t-statistics were calculated for each metric and two-sample t-statistics were calculated for each metric pair. All t-statistics were highly significant. F. Glass brain visualizations of end-timepoint IbaStrInj pathology and connectivity from ( top ) and to ( bottom ) seed regions (orange spheres). The low association between outgoing seed connectivity is in part driven by strong contralateral telencephalic and ipsilateral hindbrain projections (red boxes), which do not exhibit significant tau pathology. By contrast, the seeded regions predominantly receive connectivity from ipsilateral forebrain regions, which do exhibit pronounced tau pathology (blue box). MPI – months post injection; CP – caudoputamen; MOp – primary motor cortex. * – p < 0.05; ** – p < 0.01; *** – p < 0.001.

Article Snippet: Exploring directionality requires the use of a directed connectome, which has been previously determined using viral tracing methods in wild-type mice by the Allen Institute for Brain Science (AIBS) ); here, we use a 426-region, bilateral version of the Allen Mouse Brain Connectome Atlas (AMBCA) ( ).

Techniques: Diffusion-based Assay, Injection

Journal: bioRxiv

Article Title: Directionality bias is necessary to explain spatiotemporal progression of pathology in mouse models of tauopathy

doi: 10.1101/2024.06.04.597478

Figure Lengend Snippet: A. Scatterplot of fitted s values vs. spread rate parameter ( β ) values in the NexIS:fit-s model across all time points and experiments. Both parameters were fit individually per timepoint. There is a modest but statistically significant negative association between s and β (p < 0.05). B. Scatterplot of fitted s values vs. accumulation rate parameter ( α ) values in the NexIS:fit-s model across all time points and experiments. As in A , the s parameter was fit individually per timepoint, while alpha was fit longitudinally and fixed across timepoints (see Materials and Methods ). There is a moderately statistically significant association between s and α (p < 0.01). C. Scatterplot of α values vs. β v alues in the NexIS:fit-s model across all time points and experiments. The parameters were fit as in A and B . There is a moderately statistically significant association between α and β (p < 0.01). D . Proposed model for the interrelationships between these parameters. Overall tau pathogenicity is associated with higher accumulation and spread parameters, which are positively correlated, and is also associated with a transition from retrograde-biased to nondirectional spreading of tau along the connectome.

Article Snippet: Exploring directionality requires the use of a directed connectome, which has been previously determined using viral tracing methods in wild-type mice by the Allen Institute for Brain Science (AIBS) ); here, we use a 426-region, bilateral version of the Allen Mouse Brain Connectome Atlas (AMBCA) ( ).

Techniques:

Journal: Frontiers in Neuroinformatics

Article Title: neu Print: An open access tool for EM connectomics

doi: 10.3389/fninf.2022.896292

Figure Lengend Snippet: neuPrint graph data model. This shows the various node types and properties used for storing data relevant for connectome analysis.

Article Snippet: Consider, for example, a hypothetical mouse connectome (Abbott et al., ), a project at least a decade out.

Techniques: