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Connectomics

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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,[1][2] the retina,[3] the peripheral nervous system[4] and neuromuscular junctions.[5]

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

Diffusion magnetic resonance imaging is used to assess macroscale connectomics within the human brain. dMRI image series are used to map white matter tracts, and fMRI series are used to assess how blood flow correlates between connected gray matter areas.

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. In contrast, 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 connectivity between different brain areas. Macroscale connectomics has furthered our understanding of various brain networks including visual,[6][7] brainstem,[8][9] and language networks,[10][11] among others.

Microscale Connectomics

On the other hand, microscale connectomes focus on a much smaller area of the nervous system with much higher resolution. These datasets are commonly collected using electron microscopy imaging and offer single synapse resolution of entire local circuits. Some of the milestones in EM connectomics include the entire nervous system of C. elegans,[12] an entire fly brain,[13] and most recently a millimeter cube from both mouse[14] and human cortex.[15]

Tools

One of the main tools used for connectomics research at the macroscale level is MRI.[16] When used together, a resting-state fMRI and a dMRI dataset provide a comprehensive view of how regions of the brain are structurally connected, and how closely they are communicating.[17][18] The main tool for connectomics research at the microscale level is chemical brain preservation followed by 3D electron microscopy,[19] used for neural circuit reconstruction. Correlative microscopy, which combines fluorescence with 3D electron microscopy, results in more interpretable data as is it able to automatically detect specific neuron types and can trace them in their entirety using fluorescent markers.[20]

To see one of the first micro-connectomes at full-resolution, visit the Open Connectome Project, which is hosting several connectome datasets, including the 12TB dataset from Bock et al. (2011).

Model systems

Aside from the human brain, some of the model systems used for connectomics research are the mouse,[21] the fruit fly,[22][23] the nematode C. elegans,[24][25] and the barn owl.[26]

Caenorhabditis Elegans

The Caenorhabditis Elegans roundworm is a highly researched organism in the field of connectomics, of which a full connectome has been mapped using various imaging techniques, mainly serial-electron microscopy;[27] This process involved studying the aging process of the C. elegans brain by comparing varying worms from birth to adulthood.[28] Researchers found the biggest change with age is the wiring circuits, and that connectivity between and within brain regions increases with age.[28] Regardless of the massive achievement of mapping the full C. Elegans connectome, more information is yet to be discovered about this brain network; The researchers noted that this can be done using comparative connectomics, comparing and contrasting different species brain networks to pinpoint relations in behavior.[28]

The C. elegans has a simple nervous system, and data collection is more attainable. A study created a code that searches the connections within the C. elegans mapped connectome, as this data is already readily available. The findings were able to collect information about sensory neurons, interneurons, neck motor neurons, behavior, environmental influences and more in deep detail.[29] Overall, the experiment investigates the connection between neuroanatomy and behavior given that there is a lot of available information about the worm already discovered.[29]

To provide context, the C. elegans roundworm has 302 neurons and 5000 synaptic connections, while the human brain has 100 billion neurons and more than 100 trillion chemical synapses.[30] The human connectome has yet to be fully mapped with a limiting factor being the sheer amount of data collection that this will require in addition to the complexity in comparing individual connectomes given the great degree of variation in human neural circuits.[31]

The drosophila connectome is mostly complete, allowing a visual representation of the anatomical structure and an easier way for researchers to study drosophila neural circuits. Already, this has allowed researchers to identify differences in compartments and their electrophysiology characteristics.[32]

Mouse

An online database known as MouseLight displays over 1000 neurons mapped in the mouse brain based on a collective database of sub-micron resolution images of these brains. This platform illustrates the thalamus, hippocampus, cerebral cortex, and hypothalamus based on single cell projections.[33] Imaging technology to produce this mouse brain does not allow an in depth look at synapses, but can show axonal arborizations which contain many synapses.[34] A limiting factor to studying mouse connectomes, much like with humans, is the complexity of labeling all the cell types of the mouse brain; This is a process that would require reconstruction of 100,000+ neurons and the imaging technology is advanced enough to do so.[34]

Mice models in the lab have provided insight into genetic brain disorders, one study that manipulated mice with a deletion of 22q11.2 (chromosome 22, a likely known genetic risk factor that leads to schizophrenia).[35] Findings of this study showed that this impaired neural activity in mice’s working memory similar to as it does in humans.[35]

Leading Connectomics Labs

The Lichtman Lab of Harvard University is a leading force in the field of connectomics, investigating how synaptic connectivity and competition change with age using advanced imaging techniques on model organisms such as mice, zebra fish, and C. elegans.[36] This lab is led by neuroscientist, Harvard professor, and renowned researcher, Jeff Lichtman. The Lichtman Lab research had major impacts on the field of connectomics from their development of Brainbrow, which also had a spinoff expansion into Zebrabow technology from their work with zebra fish.[37] This is an in vivo imaging technique that uses multicolor cell labeling that allows researchers to distinguish by cell type and pathway using different Cre lines.[34]

Another notable lab is the Lee Lab of Harvard Medical School that aims to look at how neural networks are organized by function based on the observed activity of these circuits in model organisms, a field known as “functional connectomics”.[38]

Applications

A connectivity matrix assessing the functional connectivity between each brain region in the Default Mode Network (DMN). Here, shades of red indicate stronger coupling between two regions blood flow changes, and shades of blue indicate an anti-correlation between two regions.

By comparing diseased and healthy connectomes, we can gain insight into certain psychopathologies, such as neuropathic pain, and potential therapies for them. Generally, the field of neuroscience would benefit from standardization and raw data. For example, connectome maps can be used to inform computational models of whole-brain dynamics.[39][self-published source?] Current neural networks mostly rely on probabilistic representations of connectivity patterns.[40] Connectivity matrices (checkerboard diagrams of connectomics) have been used in stroke recovery to evaluate the response to treatment via Transcranial Magnetic Stimulation.[41] Similarly, connectograms (circular diagrams of connectomics) have been used in traumatic brain injury cases to document the extent of damage to neural networks.[42][43]

The human connectome can be viewed as a graph, and the rich tools, definitions and algorithms of the Graph theory can be applied to these graphs. Comparing the connectomes (or braingraphs) of healthy women and men, Szalkai et al.[44][45] have shown that in several deep graph-theoretical parameters, the structural connectome of women is significantly better connected than that of men. For example, women's connectome has more edges, higher minimum bipartition width, larger eigengap, greater minimum vertex cover than that of men. The minimum bipartition width (or, in other words, the minimum balanced cut) is a well-known measure of quality of computer multistage interconnection networks, it describes the possible bottlenecks in network communication: The higher this value is, the better is the network. The larger eigengap shows that the female connectome is better expander graph than the connectome of males. The better expanding property, the higher minimum bipartition width and the greater minimum vertex cover show deep advantages in network connectivity in the case of female braingraph.

Local measures of difference between populations of those graph have been also introduced (e.g. to compare case versus control groups).[46] Those can be found by using either an adjusted t-test,[47] or a sparsity model,[46] with the aim of finding statistically significant connections which are different among those groups.

Human connectomes have an individual variability, which can be measured with the cumulative distribution function, as it was shown in.[48] By analyzing the individual variability of the human connectomes in distinct cerebral areas, it was found that the frontal and the limbic lobes are more conservative, and the edges in the temporal and occipital lobes are more diverse. A "hybrid" conservative/diverse distribution was detected in the paracentral lobule and the fusiform gyrus. Smaller cortical areas were also evaluated: precentral gyri were found to be more conservative, and the postcentral and the superior temporal gyri to be very diverse.

Comparison to genomics

The recent advancements in the field of connectomics has sparked conversation around its relation to the field of genomics. Recently, scientists in the field have highlighted the parallels between this project and large-scale genomics initiatives.[49] Additionally, they have referenced the need for integration with other scientific disciplines, particularly genetics. While genomics focuses on the genetic blueprint of an organism, connectomics provides insights into the structural and functional connectivity of the brain. By integrating these two fields, researchers can explore how genetic variations and gene expression patterns influence the wiring and organization of neural circuits.[50] This interdisciplinary approach helps uncover the relationship between genes, neural connectivity, and brain function. Additionally, connectomics can benefit from genomics by leveraging genetic tools and techniques to manipulate specific genes or neuronal populations in order to study their impact on neural circuitry and behavior.[49] Understanding the genetic basis of neural connectivity can enhance our understanding of brain development, neural plasticity, and the mechanisms underlying various neurological disorders.

The human genome project initially faced many of the above criticisms, but was nevertheless completed ahead of schedule and has led to many advances in genetics. Some have argued that analogies can be made between genomics and connectomics, and therefore we should be at least slightly more optimistic about the prospects in connectomics.[51] Others have criticized attempts towards a microscale connectome, arguing that we don't have enough knowledge about where to look for insights, or that it cannot be completed within a realistic time frame.[52]

Human Connectome Project

The Human Connectome Project (HCP)[53] is an initiative launched in 2009 by the National Institutes of Health (NIH) with the goal of mapping the neural pathways that underlie human brain function.[54] It aimed to acquire and share data on the structural and functional connectivity of the human brain, advancing imaging and analysis techniques to improve sensitivity, resolution, and utility in the field of human connectomics.[54] By understanding the wiring patterns within and across individuals, researchers hope to unravel the electrical signals that give rise to our thoughts, emotions, and behaviors. Additional programs within the Connectome Initiative, such as the Lifespan Connectome and Disease Connectome, focus on mapping brain connections across different age groups and studying connectome variations in individuals with specific clinical diagnoses.[54] The Connectome Coordination Facility serves as a centralized repository for HCP data and provides support to researchers.[54] The success of this project has opened the door to understanding how connectomics might be influential in other areas of neuroscience. The potential of a “Connectome II” project has been referenced recently, which would focus on developing a scanner designed for high-throughput studies involving multiple subjects.[55] The project would aim to utilize recent advancements in visualization technologies towards a higher spatial resolution in imaging structural connectivity.[55] Advancements in this area might also involve incorporating wearable mobile technology to acquire various types of behavioral data, complementing the neuroimaging information gathered by the scanner.[55]

Eyewire game

Main articles: Eyewire and Sebastian Seung

Eyewire is an online game developed by American scientist Sebastian Seung of Princeton University. It uses social computing to help map the connectome of the brain. It has attracted over 130,000 players from over 100 countries.

Business Development

Omniscient Neurotechnology, a startup out of Australia co-founded by Michael Sughrue and Stephane Doyen, is currently the primary business endeavor incorporating connectomics into their development of neuronal brain maps. These maps provide a comprehensive understanding of neural connections and cell-to-cell communication, which were previously inaccessible through MRI and CT scans.[56] This technology is targeted for surgeons and researchers who might utilize the maps to identify abnormal neural connections and misfiring neurons in order to make more precise decisions regarding surgeries and therapies.[57] The technology behind the brain maps was developed using machine learning algorithms applied to MRI scans, and it has been likened to “Google Maps for the brain”.[56] They have launched two products, “Quicktome'' and “Infinitome”. Quicktome is a digital brain mapping platform intended to provide neurosurgeons with insights into a patient's brain networks before performing brain surgery, ultimately working to streamline the process of neurosurgical planning and provide greater insights for patient care.[57] The platform uses data from a single non-invasive MRI scan, which is processed automatically and delivered through an intuitive web-based app.[57] The platform allows visualization of critical brain networks and helps neurosurgeons make more informed decisions, reduce surgical uncertainty, and have better patient conversations about surgical outcomes.[57] In 2021, Quicktome received regulatory clearance from the FDA, Health Canada, and the Therapeutic Goods Administration of Australia.[58] The company’s other product, “Infinitome”, defines a neuroscience research platform that allows for revolutionary data and image analysis of the brain using computational neuroscience6. It is a cloud-based platform that complies with HIPAA regulations, and it can transform raw MRI data into detailed functional network maps of the human brain.[58] By utilizing machine learning to automate the extraction of insights from brain data specific to individual patients, Infinitome is intended to accelerate the research and clinical trial processes related to complex diseases. Ultimately, Omniscient Neurotechnology is aimed at improving the diagnosis and treatment processes of brain-related disorders. The company recently raised $30 million in Series B funding, helping to support further research and technological development.[58]

Public Datasets

Websites to explore publicly available connectomics datasets:

Macroscale Connectomics (Healthy Young Adult Datasets)

For a more comprehensive list of open macroscale datasets, check out this article

Microscale Connectomics

See also

References

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Further reading

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