A mesoscale connectome of the mouse brain

Understanding the structural organization of the brain is essential for deciphering how complex functions emerge from neural circuits. The Allen Mouse Brain Connectivity Atlas (AMBCA) has revolutionized our ability to quantify anatomical connectivity at a mesoscale resolution, bridging the gap between microscopic cellular interactions and macroscopic network organization. To leverage AMBCA for automated network construction and analysis, here, we introduce NeuroCarta, an open-source MATLAB toolbox designed to extract, process, and analyze brain-wide connectivity networks. NeuroCarta generates directed and weighted connectivity graphs, computes key network metrics, and visualizes topological features of brain circuits. As an application example, using NeuroCarta on viral tracer data from the AMBCA, we demonstrate that the mouse brain exhibits a densely connected architecture, with a degree of separation of approximately four synapses, suggesting an optimized balance between local specialization and global integration. We identify attractor nodes that may serve as key convergence points in brain-wide neural computations and show that NeuroCarta facilitates comparative network analyses, revealing regional variations in projection patterns. While the toolbox is currently constrained by the resolution and coverage of the AMBCA dataset, it provides a scalable and customizable framework for investigating brain network topology, interregional communication, and anatomical constraints on mesoscale circuit organization.

Neuroinformatics of the Allen Mouse Brain Connectivity Atlas

Mouse connectivity map to zoom in on brain's wiring problems

Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data

Brain dynamics are thought to unfold on a network determined by the pattern of axonal connections linking pairs of neuronal elements; the so-called connectome. Prior work has indicated that structural brain connectivity constrains pairwise correlations of brain dynamics ("functional connectivity"), but it is not known whether inter-regional axonal connectivity is related to the intrinsic dynamics of individual brain areas. Here we investigate this relationship using a weighted, directed mesoscale mouse connectome from the Allen Mouse Brain Connectivity Atlas and resting state functional MRI (rs-fMRI) time-series data measured in 184 brain regions in eighteen anesthetized mice. For each brain region, we measured degree, betweenness, and clustering coefficient from weighted and unweighted, and directed and undirected versions of the connectome. We then characterized the univariate rs-fMRI dynamics in each brain region by computing 6930 time-series properties using the time-series analysis toolbox, hctsa. After correcting for regional volume variations, strong and robust correlations between structural connectivity properties and rs-fMRI dynamics were found only when edge weights were accounted for, and were associated with variations in the autocorrelation properties of the rs-fMRI signal. The strongest relationships were found for weighted in-degree, which was positively correlated to the autocorrelation of fMRI time series at time lag τ = 34 s (partial Spearman correlation ρ=0.58), as well as a range of related measures such as relative high frequency power (f > 0.4 Hz: ρ=-0.43). Our results indicate that the topology of inter-regional axonal connections of the mouse brain is closely related to intrinsic, spontaneous dynamics such that regions with a greater aggregate strength of incoming projections display longer timescales of activity fluctuations.

Diffusion MRI tractography is the only noninvasive method to measure the structural connectome in humans. However, recent validation studies have revealed limitations of modern tractography approaches, which lead to significant mistracking caused in part by local uncertainties in fiber orientations that accumulate to produce larger errors for longer streamlines. Characterizing the role of this length bias in tractography is complicated by the true underlying contribution of spatial embedding to brain topology. In this work, we compare graphs constructed with ex vivo tractography data in mice and neural tracer data from the Allen Mouse Brain Connectivity Atlas to random geometric surrogate graphs which preserve the low-order distance effects from each modality in order to quantify the role of geometry in various network properties. We find that geometry plays a substantially larger role in determining the topology of graphs produced by tractography than graphs produced by tracers. Tractography underestimates weights at long distances compared to neural tracers, which leads tractography to place network hubs close to the geometric center of the brain, as do corresponding tractography-derived random geometric surrogates, while tracer graphs place hubs further into peripheral areas of the cortex. We also explore the role of spatial embedding in modular structure, network efficiency and other topological measures in both modalities. Throughout, we compare the use of two different tractography streamline node assignment strategies and find that the overall differences between tractography approaches are small relative to the differences between tractography- and tracer-derived graphs. These analyses help quantify geometric biases inherent to tractography and promote the use of geometric benchmarking in future tractography validation efforts.

The corpus callosum (CC) is the largest white matter structure and the primary pathway for interhemispheric brain communication. Investigating callosal connectivity is crucial to unraveling the brain's anatomical and functional organization in health and disease. Classical anatomical studies have characterized the bulk of callosal axonal fibers as connecting primarily homotopic cortical areas. Whenever detected, heterotopic callosal fibers were ascribed to altered sprouting and pruning mechanisms in neurodevelopmental diseases such as CC dysgenesis (CCD). We hypothesized that these heterotopic connections had been grossly underestimated due to their complex nature and methodological limitations. We used the Allen Mouse Brain Connectivity Atlas and high-resolution diffusion-weighted imaging to identify and quantify homotopic and heterotopic callosal connections in mice, marmosets, and humans. In all 3 species, we show that ~75% of interhemispheric callosal connections are heterotopic and comprise the central core of the CC, whereas the homotopic fibers lay along its periphery. We also demonstrate that heterotopic connections have an essential role in determining the global properties of brain networks. These findings reshape our view of the corpus callosum's role as the primary hub for interhemispheric brain communication, directly impacting multiple neuroscience fields investigating cortical connectivity, neurodevelopment, and neurodevelopmental disorders.

The wiring diagram of the mouse brain has recently been mapped at a mesoscopic scale in the Allen Mouse Brain Connectivity Atlas. Axonal projections from brain regions were traced using green fluoresent proteins. The resulting data were registered to a common three-dimensional reference space. They yielded a matrix of connection strengths between 213 brain regions. Global features such as closed loops formed by connections of similar intensity can be inferred using tools from persistent homology. We map the wiring diagram of the mouse brain to a simplicial complex (filtered by connection strengths). We work out generators of the first homology group. Some regions, including nucleus accumbens, are connected to the entire brain by loops, whereas no region has non-zero connection strength to all brain regions. Thousands of loops go through the isocortex, the striatum and the thalamus. On the other hand, medulla is the only major brain compartment that contains more than 100 loops.

Deciphering the Neural and Molecular Mechanisms of C. elegans Behavior

A flexible software framework and post hoc cell type discrimination for in vivo two-photon calcium imaging of neuronal population activity

Reconstructing the human brain's wiring diagram from axons up.

A reconstruction approach is proposed based on a laser plane derived from the Random Sample Consensus (RANSAC) and three dimensional (3D) reference. The Hessian matrix is performed to extract the center points of the two intersection lines between the laser plane and the reference. The initial laser points are divided into two parts: the inner and exterior points. The inner points are chosen by the RANSAC method to fit the laser line, which avoids the interference of the exterior points. The intersection lines between the laser plane and the reference are represented by Plucker matrices. The laser plane is modeled by the intersection laser lines in the coordinate system of the reference and solved by the singular value decomposition method. The cylinders with known radii are employed to evaluate the accuracy of the laser plane, by comparing the reconstructed radii with the real values of the cylinders. The effects of the Gaussian noise, the distance from the camera to the 3D reference and the cylinder radius are investigated in the experiments. The average reconstruction errors of the three methods of RANSAC, Hough and least squares are 0.68, 1.47 and 2.32 mm in the four groups of experiments, respectively. The experimental results show that reconstruction method using the laser plane generated from the RANSAC and the 3D reference is reliable to present a high accuracy reconstruction for the vision reconstruction system.

In neocortex, Nav1.1 is expressed in neocortical pyramidal tract projection neurons and a minor subpopulation of cortico-cortical projection neurons in addition to its predominant expression in inhibitory neurons, while the majority of cortico-thalamic, cortico-striatal, and cortico-cortical neurons express Nav1.2.

In neocortex, Nav1.1 is expressed in neocortical pyramidal tract projection neurons and a minor subpopulation of cortico-cortical projection neurons in addition to its predominant expression in inhibitory neurons, while the majority of cortico-thalamic, cortico-striatal, and cortico-cortical neurons express Nav1.2.

The prefrontal cortex (PFC), a region responsible for high-order cognitive functions, such as decision-making, attention and working memory, is highly influenced by stress and corticosteroid stress hormones. Recently it has been shown that acute stress affects PFC functions by potentiating glutamatergic transmission via a mechanism dependent on glucocorticoid receptor (GR) and its downstream target, serum and glucocorticoid-inducible kinase (SGK). To identify the key regulators of stress responses, we examined the role of histone deacetylase 6 (HDAC6), a unique member of the HDAC family that could regulate the GR chaperone protein heat shock protein 90 (HSP90), in the synaptic action of acute stress in PFC. We found that HDAC6 inhibition or knockdown blocked the enhancement of glutamatergic transmission and glutamate receptor trafficking by acute stress in vivo or corticosterone treatment in vitro. In addition, HDAC6 inhibition blocked the up-regulation of SGK in animals exposed to acute stress. HSP90 inhibition or knockdown produced a similar blockade of the acute stress-induced enhancement of glutamatergic signalling. These findings have identified HDAC6 as a key molecule gating the effects of acute stress on synaptic functions in the PFC.