BrainX3 is a large-scale simulation of human brain activity with real-time interaction, rendered in 3D in a virtual reality environment, which combines computational power with human intuition for the exploration and analysis of complex dynamical networks. We ground this simulation on structural connectivity obtained from diffusion spectrum imaging data and model it on neuronal population dynamics. Users can interact with BrainX3 in real-time by perturbing brain regions with transient stimulations to observe reverberating network activity, simulate lesion dynamics or implement network analysis functions from a library of graph theoretic measures. BrainX3 can thus be used as a novel immersive platform for exploration and analysis of dynamical activity patterns in brain networks, both at rest or in a task-related state, for discovery of signaling pathways associated to brain function and/or dysfunction and as a tool for virtual neurosurgery. Our results demonstrate these functionalities and shed insight on the dynamics of the resting-state attractor. Specifically, we found that a noisy network seems to favor a low firing attractor state. We also found that the dynamics of a noisy network is less resilient to lesions. Our simulations on TMS perturbations show that even though TMS inhibits most of the network, it also sparsely excites a few regions. This is presumably due to anti-correlations in the dynamics and suggests that even a lesioned network can show sparsely distributed increased activity compared to healthy resting-state, over specific brain areas.
The claustrum seems to have been waiting for the science of connectomics. Due to its tiny size, the structure has remained remarkably difficult to study until modern technological and mathematical advancements like graph theory, connectomics, diffusion tensor imaging, HARDI, and excitotoxic lesioning. That does not mean, however, that early methods allowed researchers to assess micro-connectomics. In fact, the claustrum is such an enigma that the only things known for certain about it are its histology, and that it is extraordinarily well connected. In this literature review, we provide background details on the claustrum and the history of its study in the human and in other animal species. By providing an explanation of the neuroimaging and histology methods have been undertaken to study the claustrum thus far—and the conclusions these studies have drawn—we illustrate this example of how the shift from micro-connectomics to macro-connectomics advances the field of neuroscience and improves our capacity to understand the brain.
The somatotopically organized whisker barrel field of the rat primary somatosensory (S1) cortex is a commonly used model system for anatomical and physiological investigations of sensory processing. The neural connections of the barrel cortex have been extensively mapped. But most investigations have focused on connections to limited regions of the brain, and overviews in the literature of the connections across the brain thus build on a range of material from different laboratories, presented in numerous publications. Furthermore, given the limitations of the conventional journal article format, analyses and interpretations are hampered by lack of access to the underlying experimental data. New opportunities for analyses have emerged with the recent release of an online resource of experimental data consisting of collections of high-resolution images from 6 experiments in which anterograde tracers were injected in S1 whisker or forelimb representations. Building on this material, we have conducted a detailed analysis of the brain wide distribution of the efferent projections of the rat barrel cortex. We compare our findings with the available literature and reports accumulated in the Brain Architecture Management System (BAMS2) database. We report well-known and less known intracortical and subcortical projections of the barrel cortex, as well as distinct differences between S1 whisker and forelimb related projections. Our results correspond well with recently published overviews, but provide additional information about relative differences among S1 projection targets. Our approach demonstrates how collections of shared experimental image data are suitable for brain-wide analysis and interpretation of connectivity mapping data.