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.
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.
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).
[8] Established methods of brain research, such as axonal tracing, provided early avenues for building connectome data sets.
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.
[10] Accurate parcellation allows each node in the macroscale connectome to be more informative by associating it with a distinct connectivity pattern and functional profile.
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)[10] and the thalamocortical system in the feline brain (Scannell et al., 1999).
The online macaque cortex connectivity tool CoCoMac (Kötter, 2004)[14] and the temporal lobe connectome of the rat[15] are prominent examples of such a database.
To address the data collection issues, several groups are building high-throughput serial electron microscopes (Kasthuri et al., 2009; Bock et al. 2011).
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).
Mapping the connectome at the cellular level in vertebrates currently requires post-mortem (after death) microscopic analysis of limited portions of brain tissue.
[19] However, applications to larger tissue blocks of entire nervous systems have traditionally had difficulty with projections that span longer distances.
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.
[18] Zador's technique, called BOINC (barcoding of individual neuronal connections) uses high-throughput DNA sequencing to map neural circuits.
For the macro-scale connectome, the nodes correspond to the ROIs (regions of interest), while the edges of the graph are derived from the axons interconnecting those areas.
One group of researchers (Iturria-Medina et al., 2008)[35] has constructed connectome data sets using diffusion tensor imaging (DTI)[36][37] followed by the derivation of average connection probabilities between 70 and 90 cortical and basal brain gray matter areas.
[43] The combination of whole-head DSI datasets acquired and processed according to the approach developed by Hagmann et al. (2007)[39] with the graph analysis tools conceived initially for animal tracing studies (Sporns, 2006; Sporns, 2007)[44][45] allow a detailed study of the network structure of human cortical connectivity (Hagmann et al., 2008).
Hagmann et al. presented evidence for the existence of a structural core of highly and mutually interconnected brain regions, located primarily in posterior medial and parietal cortex.
Hence, algorithms to find local difference between graph populations have also been introduced (e.g. to compare case versus control groups).
For example, in the C. elegans connectome, the total number of synapses increases 5-fold from birth to adulthood, changing both local and global network properties.
[58] Evidence for this level of rewiring comes from observations that local circuits form new connections as a result of experience-dependent plasticity in the visual cortex.
Additionally, the number of local connections between pyramidal neurons in the primary somatosensory cortex increases following altered whisker sensory experience in rodents.
[64][65][66] Changes can even be seen within five hours on apical tufts of layer five pyramidal neurons in the primary motor cortex after a seed reaching task in primates.
[67][68] Moreover, connectome-based methods have had an impact on planning or understanding therapeutic options, such as invasive and noninvasive brain stimulation[disambiguation needed] procedures.
[69][70][71] In this context, the term 'connectomic surgery' was introduced in 2012,[72] as a framework to define or refine surgical targets by identifying pathological brain circuits using neuroimaging techniques such as diffusion-imaging based tractography that are also leveraged for macroscale connectomics.
[19] Based on this seminal work, the first ever connectome (then called "neural circuitry database" by the authors) for C. elegans was published in book form with accompanying floppy disks by Achacoso and Yamamoto in 1992.
Its central nervous system (CNS) is notably compact, housing approximately 200,000 neurons in adults, yet it exhibits reasonably stereotyped neural connections across individual flies.
Obtaining an anatomical dataset of the fly's CNS could be a pivotal step, potentially offering insights into the nervous systems of other organisms.
A full electron microscopy (EM) connectome of the larval brain of D. melanogaster, including 3016 neurons and 548,000 synapses, was published in March 2023.
