Brain networks can be defined at different levels of scale, corresponding to levels of spatial resolution in brain imaging (Kötter, 2007, Sporns, 2010). These scales can be roughly categorized as macroscale, mesoscale and microscale. Ultimately, it may be possible to join<unk> c maps obtained at different scales into a single hierarchical map of the neural organization of a given species that ranges from single neurons to populations of neurons to larger systems like cortical areas. Given the methodological uncertainties involved in inferring connectivity from the primary experimental data, and given that there are likely to be large differences in the<unk> s of different individuals, any unified map will likely rely on probabilistic representations of connectivity data (Sporns et al., 2005). Macroscale A<unk>  at the macroscale (millimeter resolution) attempts to capture large brain systems that can be parcellated into anatomically distinct modules (areas, parcels or nodes), each having a distinct pattern of connectivity.<unk> c databases at the mesoscale and macroscale may be significantly more compact than those at cellular resolution, but they require effective strategies for accurate anatomical or functional parcellation of the neural volume into network nodes (for complexities see, e.g., Wallace et al., 2004). Established methods of brain research, such as axonal tracing, provided early avenues for building<unk>  data sets. However, more recent advances in living subjects has been made by the use of non-invasive imaging technologies such as diffusion-weighted magnetic resonance imaging (DW-MRI) and functional magnetic resonance imaging (fMRI). The first, when combined with tractography allows reconstruction of the major fiber bundles in the brain. The second allows the researcher to capture the brain's network activity (either at rest or while performing directed tasks), enabling the identification of structurally and anatomically distinct areas of the brain that are functionally connected. Notably, the goal of the Human<unk>  Project, led by the WU-Minn consortium, is to build a structural and functional map of the healthy human brain at the macro scale, using a combination of multiple imaging technologies and resolutions. Recent advances in connectivity mapping DTI Throughout the 2000s, several investigators have attempted to map the large-scale structural architecture of the human cerebral cortex. One attempt exploited cross-correlations in cortical thickness or volume across individuals (He et al., 2007). Such gray-matter thickness correlations have been postulated as indicators for the presence of structural connections. A drawback of the approach is that it provides highly indirect information about cortical connection patterns and requires data from large numbers of individuals to derive a single connection data set across a subject group. Other investigators have attempted to build whole-brain connection matrices from DW-MRI imaging data. The Blue Brain Project is attempting to reconstruct the entire mouse<unk>  using a diamond knife sharpened to an atomic edge, and electron microscopy for imaging tissue slices. Challenge for macroscale<unk> cs The initial explorations in macroscale human<unk> cs were done using either equally sized regions or anatomical regions with unclear relationship to the underlying functional organization of the brain (e.g. gyral and sulcal-based regions). While much can be learned from these approaches, it is highly desirable to parcellate the brain into functionally distinct parcels: brain regions with distinct architectonics, connectivity, function, and/or topography (Felleman and Van Essen, 1991). Accurate parcellation allows each node in the macroscale<unk>  to be more informative by associating it with a distinct connectivity pattern and functional profile. Parcellation of localized areas of cortex have been accomplished using diffusion tractography (Beckmann et al. 2009) and functional connectivity (Nelson et al. 2010) to non-invasively measure connectivity patterns and define cortical areas based on distinct connectivity patterns. Such analyses may best be done on a whole brain scale and by integrating non-invasive modalities. Accurate whole brain parcellation may lead to more accurate macroscale<unk> s for the normal brain, which can then be compared to disease states. Pathways through cerebral white matter can be charted by histological dissection and staining, by degeneration methods, and by axonal tracing. Axonal tracing methods form the primary basis for the systematic charting of long-distance pathways into extensive, species-specific anatomical connection matrices between gray matter regions. Landmark studies have included the areas and connections of the visual cortex of the macaque (Felleman and Van Essen, 1991) and the thalamocortical system in the feline brain (Scannell et al., 1999). The development of neuroinformatics databases for anatomical connectivity allow for continual updating and refinement of such anatomical connection maps. The online macaque cortex connectivity tool CoCoMac (Kötter, 2004) and the temporal lobe<unk>  of the rat are prominent examples of such a database. Mesoscale A "mesoscale"<unk>  corresponds to a spatial resolution of hundreds of micrometers. Rather than attempt to map each individual neuron, a<unk>  at the mesoscale would attempt to capture anatomically and/or functionally distinct neuronal populations, formed by local circuits (e.g. cortical columns) that link hundreds or thousands of individual neurons. This scale still presents a very ambitious technical challenge at this time and can only be probed on a small scale with invasive techniques or very high field magnetic resonance imaging (MRI) on a local scale. Microscale Mapping the<unk>  at the "microscale" (micrometer resolution) means building a complete map of the neural systems, neuron-by-neuron. The challenge of doing this becomes obvious: the number of neurons comprising the brain easily ranges into the billions in more complex organisms. The human cerebral cortex alone contains on the order of 1010 neurons linked by 1014 synaptic connections. By comparison, the number of base-pairs in a human genome is 3×109. A few of the main challenges of building a human<unk>  at the microscale today include: data collection would take years given current technology, machine vision tools to annotate the data remain in their infancy, and are inadequate, and neither theory nor algorithms are readily available for the analysis of the resulting brain-graphs. To address the data collection issues, several groups are building high-throughput serial electron microscopes (Kasthuri et al., 2009; Bock et al. 2011). To address the machine-vision and image-processing issues, the Open<unk>  Project is alg-sourcing (algorithm outsourcing) this hurdle. Finally, statistical graph theory is an emerging discipline which is developing sophisticated pattern recognition and inference tools to parse these brain-graphs (Goldenberg et al., 2009). Current non-invasive imaging techniques cannot capture the brain's activity on a neuron-by-neuron level. Mapping the<unk>  at the cellular level in vertebrates currently requires post-mortem (after death) microscopic analysis of limited portions of brain tissue. Non-optical techniques that rely on high-throughput DNA sequencing have been proposed recently by Anthony Zador (CSHL). Traditional histological circuit-mapping approaches rely on imaging and include light-microscopic techniques for cell staining, injection of labeling agents for tract tracing, or chemical brain preservation, staining and reconstruction of serially sectioned tissue blocks via electron microscopy (EM). Each of these classical approaches has specific drawbacks when it comes to deployment for<unk> cs. The staining of single cells, e.g. with the Golgi stain, to trace cellular processes and connectivity suffers from the limited resolution of light-microscopy as well as difficulties in capturing long-range projections. Tract tracing, often described as the "gold standard" of neuroanatomy for detecting long-range pathways across the brain, generally only allows the tracing of fairly large cell populations and single axonal pathways. EM reconstruction was successfully used for the compilation of the C. elegans<unk>  (White et al., 1986). However, applications to larger tissue blocks of entire nervous systems have traditionally had difficulty with projections that span longer distances. Recent advances in mapping neural connectivity at the cellular level offer significant new hope for overcoming the limitations of classical techniques and for compiling cellular<unk>  data sets (Livet et al., 2007; Lichtman et al., 2008). Using Brainbow, a combinatorial color labeling method based on the stochastic expression of several fluorescent proteins, Jeff W. Lichtman and colleagues were able to mark individual neurons with one of over 100 distinct colors. The labeling of individual neurons with a distinguishable hue then allows the tracing and reconstruction of their cellular structure including long processes within a block of tissue. In March 2011, the journal Nature published a pair of articles on micro-connectomes: Bock et al. and Briggman et al. In both articles, the authors first characterized the functional properties of a small subset of cells, and then manually traced a subset of the processes emanating from those cells to obtain a partial subgraph. In alignment with the principles of open science, the authors of Bock et al. (2011) have released their data for public access. The full resolution 12 terabyte dataset from Bock et al. is available at NeuroData. In 2012, a citizen science project called EyeWire began attempting to crowdsource the mapping of the<unk>  through an interactive game. Independently, important topologies of functional interactions among several hundred cells are also gradually going to be declared (Shimono and Beggs, 2014). Scaling up ultrastructural circuit mapping to the whole mouse brain is currently underway (Mikula, 2012). An alternative approach to mapping connectivity was recently proposed by Zador and colleagues (Zador et al., 2012). Zador's technique, called BOINC (barcoding of individual neuronal connections)
Connectome
Trabectome
Microkeratome
Nuclear bag fiber
TheraSphere
NeuroMatrix
Neuronal ensemble
Neuromere