The recent explosion in data acquisition, although certainly not unique to the neurosciences, has positioned the brain to be one of the most probed, but least understood subjects in science. Everyday countless data sets cataloging the neural activity recorded by microelectrode arrays, the pathways illuminated by retrograde tracing, or the pathological markers inferred from genome-wide association studies, along with new computational tools to visualize and analyze this data, are made publicly available online, yet a global account of brain functioning remains elusive. Adhering to the biological dogma that structure determines function, many neuroscientists set out to untangle the connectome – a complete account of all of the connections made within the nervous system.
Such a wiring diagram is believed to be necessary, but not sufficient, to understand the nervous system, by providing a static structural framework for analyzing dynamic interactions at the level of neurons, circuits, or cortical regions. In this sense it would play the same role as the genome has in understanding complex interactions between genetic elements. However, due to the enormity of the data set for humans (~1014 synapses), and the scale of individual connections (~20 nm across the synaptic cleft), only the connectome of a single species, the roundworm C. elegans with 302 neurons, has been completely described. Despite these challenges, many researchers believe progress is possible by implementing a hierarchical approach to systematically characterize the structure of the mammalian nervous system.
Dr. Larry Swanson of the University of Southern California, in collaboration with fellow connectomics researchers, has proposed performing connectivity analyses at multiple spatial scales to reconstruct a connectome by interpolating across the spatial and temporal resolutions explored by various technologies. To be feasible, the scales must form a nested hierarchy, and so a macroconnection is defined to be between two gray-matter regions, a mesoconnection between two neuron types, and a microconnection between two individual neurons. Although, most mammalian connectomics research is still focused on the macro-scale, Swanson believes such a macroconnectome will detail global organization themes that will help to map data at finer scales. In particular Dr. Swanson has focused on the connectome of the albino, adult rat, and has had success in elucidating the macroconnection architecture of the cerebral cortex and nuclei.
The macroconnectomes of the rat cerebral cortex and cerebral nuclei were constructed by mining the primary literature for evidence of macroconnections, and network analyses were performed to identify distinctive architectural features. Both of the macroconnectomes were found to be fairly dense, with ~40% of all possible macroconnections present, to have four distinct hubs (regions much more highly connected than in the network on average), and a rich club (group of highly connected regions that connect to other highly connected regions) containing the four hubs. This means that both networks should be quite robust to lesion of any single member the rich club, as their high degree of connectivity implies redundant processing. However, the cortex was shown to exhibit greater small-world attributes and reciprocity (prevalence of symmetric connections) than the cerebral nuclei network. This supports the view that the cortex has a greater role in the efficient integration of global information, while the cerebral nuclei network mediate information flow in specific circuits between the cortex and subcortical regions.
Macroconnectomics, at the very least, provides neuroscientists a much more manageable way to visualize a rich and diverse set of anatomical data. In addition to generating complicated connection graphs, identifying hubs or modules can serve to reduce the dimensionality of the data being considered (see the top figure), and directs attention towards brain regions with potentially greater functional roles. Network analyses also provide a robust, well-defined strategy for the structural comparison of networks both within a single species (as was done here) and potentially across species. Dr. Swanson even believes that, due to the relatively conserved structure of cortical regions across mammals, the architectural principles found from rat macroconnectome analyses could be used as proxies for human cortical networks until new experimental techniques allow us to probe the human brain more directly.
This hierarchical research strategy is perfectly positioned to take full advantage of the mountains of neural data currently available, as well as adapt to and integrate future avalanches, in order to eventually provide a robust framework to consider neural connectivity. In the mean time, it can also provide useful tools and databases for researchers concerned with connectivity, and generate testable hypotheses about human cortical connections based on other model mammalian systems.
Please come join us on Tuesday, November 1st, at 4pm in the CNCB Marilyn Farquhar Seminar Room to hear more about this exciting avenue of research from Dr. Larry Swanson.
Ryan Golden is a first-year student in the neurosciences graduate program rotating in Dr. Bradley Voytek’s lab. His interests currently lie in neural computation, how network architecture constrains information processing, and how neurommodulation influences plasticity.