connectome connectome a connectome (/kəˈnɛktoʊm/) is a comprehensive map of neural connections in the brain, and may be thought of as its "wiring diagram". an organism's nervous system is made up of neurons which communicate through synapses. a connectome is constructed by tracing the neuron in a nervous system and mapping where neurons are connected through synapses. white matter tracts within a human brain, as visualized by mri tractography rendering of a group connectome based on 20 subjects. anatomical fibers that constitute the white matter architecture of the human brain are visualized color-coded by traversing direction (xyz-directions mapping to rgb colors respectively). visualization of fibers was done using trackvis software. the significance of the connectome stems from the realization that the structure and function of the human brain are intricately linked, through multiple levels and modes of brain connectivity. there are strong natural constraints on which neurons or neural populations can interact, or how strong or direct their interactions are. indeed, the foundation of human cognition lies in the pattern of dynamic interactions shaped by the connectome. despite such complex and variable structure-function mappings, the connectome is an indispensable basis for the mechanistic interpretation of dynamic brain data, from single-cell recordings to functional neuroimaging. origin and usage of the term in 2005, dr. olaf sporns at indiana university and dr. patric hagmann at lausanne university hospital independently and simultaneously suggested the term "connectome" to refer to a map of the neural connections within the brain. this term was directly inspired by the ongoing effort to sequence the human genetic code—to build a genome. "connectomics" (hagmann, 2005) has been defined as the science concerned with assembling and analyzing connectome data sets. in their 2005 paper, "the human connectome, a structural description of the human brain", sporns et al. wrote: to understand the functioning of a network, one must know its elements and their interconnections. the purpose of this article is to discuss research strategies aimed at a comprehensive structural description of the network of elements and connections forming the human brain. we propose to call this dataset the human "connectome," and we argue that it is fundamentally important in cognitive neuroscience and neuropsychology. the connectome will significantly increase our understanding of how functional brain states emerge from their underlying structural substrate, and will provide new mechanistic insights into how brain function is affected if this structural substrate is disrupted. in his 2005 ph.d. thesis, from diffusion mri to brain connectomics, hagmann wrote: it is clear that, like the genome, which is much more than just a juxtaposition of genes, the set of all neuronal connections in the brain is much more than the sum of their individual components. the genome is an entity it-self, as it is from the subtle gene interaction that emerges. in a similar manner, one could consider the brain connectome, set of all neuronal connections, as one single entity, thus emphasizing the fact that the huge brain neuronal communication capacity and computational power critically relies on this subtle and incredibly complex connectivity architecture. the term "connectome" was more recently popularized by sebastian seung's i am my connectome speech given at the 2010 ted conference, which discusses the high-level goals of mapping the human connectome, as well as ongoing efforts to build a three-dimensional neural map of brain tissue at the microscale. in 2012, seung published the book connectome: how the brain's wiring makes us who we are. methods 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 connectomic 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 connectomes of different individuals, any unified map will likely rely on probabilistic representations of connectivity data (sporns et al., 2005). macroscale a connectome 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. connectomic 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 connectome 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 connectome 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 tractographic reconstruction of neural connections via 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 connectome using a diamond knife sharpened to an atomic edge, and electron microscopy for imaging tissue slices. challenge for macroscale connectomics the initial explorations in macroscale human connectomics 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 connectome 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 connectomes 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 connectome of the rat are prominent examples of such a database. mesoscale a "mesoscale" connectome corresponds to a spatial resolution of hundreds of micrometers. rather than attempt to map each individual neuron, a connectome 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 connectome 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 connectome 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 connectome 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 connectome 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 connectomics. 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 connectome (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 connectome 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 connectome 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