Connectomics is the production and study of connectomes: comprehensive maps of connections within an organism's nervous system. More generally, it can be thought of as the study of neuronal wiring diagrams with a focus on how structural connectivity, individual synapses, cellular morphology, and cellular ultrastructure contribute to the make up of a network. The nervous system is a network made of billions of connections and these connections are responsible for our thoughts, emotions, actions, memories, function and dysfunction. Therefore, the study of connectomics aims to advance our understanding of mental health and cognition by understanding how cells in the nervous system are connected and communicate. Because these structures are extremely complex, methods within this field use a high-throughput application of functional and structural neural imaging, most commonly magnetic resonance imaging (MRI), electron microscopy, and histological techniques in order to increase the speed, efficiency, and resolution of these nervous system maps. To date, tens of large scale datasets have been collected spanning the nervous system including the various areas of cortex, cerebellum, the retina, the peripheral nervous system and neuromuscular junctions.
Generally speaking, there are two types of connectomes; macroscale and microscale. Macroscale connectomics refers to using functional and structural MRI data to map out large fiber tracts and functional gray matter areas within the brain in terms of blood flow (functional) and water diffusivity (structural). Microscale connectomics is the mapping of small organisms' complete connectome using microscopy and histology. That is, all connections that exist in their central nervous system.
Methods
Macroscale Connectomics
Macroscale connectomes are commonly collected using diffusion-weighted magnetic resonance imaging (dMRI or DW-MRI) and functional magnetic resonance imaging (fMRI). dMRI datasets can span the entire brain, imaging white matter between the cortex and subcortex, providing information about the diffusion of water molecules in brain tissue, and allowing researchers to infer the orientation and integrity of white matter pathways. dMRI can be used in conjunction with tractography where it enables the reconstruction of white matter tracts in the brain. It does so by measuring the diffusion of water molecules in multiple directions, as dMRI can estimate the local fiber orientations and generate a model of the brain's fiber pathways. Meanwhile, tractography algorithms trace the likely trajectories of these pathways, providing a representation of the brain's anatomical connectivity. Metrics such as fractional anisotropy (FA), mean diffusivity (MD), or connectivity strength can be computed from dMRI data to assess the microstructural properties of white matter and quantify the strength of (long-range) connections between brain regions.
In contrast to dMRI, fMRI datasets measure cerebral blood flow in the brain, as a marker of neuronal activation. One of the benefits of MRI is it offers in vivo information about the connectivity between different brain areas. fMRI measures the blood oxygenation level-dependent (BOLD) signal, which reflects changes in cerebral blood flow and oxygenation associated with neural activity, as regulated by the neurovascular unit. Resting-state functional connectivity (RSFC) analysis is a common method to measure connectomes using fMRI that involves acquiring fMRI data while the subject is at rest and not performing any specific tasks or stimuli. RSFC examines the temporal correlation of the BOLD signals between different brain regions (after accounting for the confounding effect of other regions), providing insights into functional connectivity.