[go: up one dir, main page]
Wikipedia

Connectomics

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

edit
 
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

edit

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.[6] dMRI can be used in conjunction with tractography where it enables the reconstruction of white matter tracts in the brain.[6] 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.[6] Meanwhile, tractography algorithms trace the likely trajectories of these pathways, providing a representation of the brain's anatomical connectivity.[6] 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.[7]

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.[8] 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.[9] 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.[8]

Neuromodulation allows clinicians to utilize stimulatory techniques to treat neurological and psychiatric disorders, such as major depressive disorder (MDD), Alzheimer's, and schizophrenia while providing insights into the connectome.[10] Specifically, Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that applies strong magnetic pulses between scalp electrodes which target specific brain regions with electrical currents.[11] This can temporarily disrupt or enhance the activity of specific brain areas and observe changes in connectivity.[11] Transcranial direct current stimulation (tDCS) is another non-invasive neuromodulation technique that applies a constant but relatively weak electrical current for a few minutes, modulating neuronal excitability.[12] It allows researchers to investigate the causal relationship between targeted brain regions and changes in connectivity.[12] tDCS increases the functional connectivity within the brain, with a bias towards specific networks (e.g., cortical processing), and may even cause structural changes to take place in the white matter via myelination and in the gray matter via synaptic plasticity.[12] Another neuromodulation technique is deep brain stimulation (DBS), an invasive technique that involves surgically implanting electrodes into specific brain regions in order to apply localized, high-frequency electrical impulses.[13] This technique modulates brain networks and is often used to alleviate motor symptoms from disorders like Parkinson's, essential tremor, and dystonia.[14] The functional and structural connectivity between electrodes can be used to predict patient outcomes and estimate optimal connectivity profiles.[13]

Electrophysiological Methods

edit

Electrophysiological methods measure the difference in signals from different parts of the brain to estimate the connectivity between them, a process that requires a low signal-to-noise ratio to maintain the accuracy of the measurements and sufficient spatial resolution to support the connectivity between specific regions of the brain.[15] These methods offer insights into real-time neural dynamics and functional connectivity between brain regions. Electroencephalography (EEG) measures the differences in the electrical potential generated by oscillating currents at the surface of the scalp, due to the non-invasive, external placement of the electrodes.[16] Meanwhile, magnetoencephalography (MEG) relies on the magnetic fields generated by the electrical activity of the brain to collect information.[16]

Macroscale connectomics has furthered our understanding of various brain networks including visual,[17][18] brainstem,[19][20] and language networks,[21][22] among others.

Microscale Connectomics

edit

On the other hand, microscale connectomes focus on resolving individual cell-to-cell connectivity within much smaller volumes of nervous system tissue. These datasets are commonly collected using electron microscopy (EM) and offer single synapse resolution. The first microscale connectome encompassing an entire nervous system was produced for the nematode C. elegans in 1986.[23] This was done by manually annotating printouts of the EM scans.[23] Advances in EM acquisition, image alignment and segmentation, and manipulation of large datasets have since allowed for larger volumes to be imaged and segmented more easily. EM has been used to produce connectomes from a variety of nervous system samples, including publicly available datasets that encompass the entire brain[24] and ventral nerve cord[25][26] of adult Drosophila melanogaster, the full central nervous system (connected brain and ventral nerve cord) of larval Drosophila melanogaster,[27] and volumes from mouse[28] and human cortex.[29][30] The National Institutes of Health (NIH) has now invested in creating an EM connectome of an entire mouse brain.[31]

Electron microscopy is the imaging technique that provides the highest spatial resolution, which is crucial for being able to recover presynaptic and postsynaptic sites as well as fine morphological details. However, other imaging modalities are approaching the nanometer-scale resolution necessary for microscale connectomics. X-ray nanotomography using a synchrotron source can now reach <100 nm resolution, and can theoretically continue to improve.[32] Unlike EM, this technique does not require the tissue being imaged to be stained with heavy metals or to be physically sectioned.[32] Conventional light microscopy is constrained by light diffraction. Researchers have recently used stimulated emission depletion (STED) microscopy, a super-resolution light microscopy technique, to image the extracellular space of a sample from mouse hippocampus, allowing for reconstruction of all neurites within this volume.[33] They then re-imaged the same tissue for fluorescently-tagged synaptic markers to find synaptic connectivity in the sample.[33] However, this approach was limited to ~130 nm resolution, and was therefore not able resolve thin axons.[33]

Tools

edit

One of the main tools used for connectomics research at the macroscale level is MRI.[34] 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.[35][36]

The main tool for connectomics research at the microscale level is chemical brain preservation followed by 3D electron microscopy,[37] 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.[38]

In addition to advanced microscopy techniques, connectomics heavily relies on software analysis tools and machine learning pipelines for reconstructing and analyzing neural networks. These tools are designed to process and interpret the vast amounts of data generated by volume electron microscopy and other imaging methods. Key steps in connectomic reconstruction include image segmentation, where individual neurons and their components are identified and annotated, and network mapping, where the connections between these neurons are established.[39]

Several software platforms facilitate these tasks. CATMAID (Collaborative Annotation Toolkit for Massive Amounts of Image Data) is a decentralized web interface allowing seamless navigation of large image stacks. It is designed to facilitate the collaborative exploration, annotation, and efficient sharing of regions of interests by bookmarking.[40] Another example is WEBKNOSSOS, an online platform used for viewing, annotating, and sharing large 3D images, aiding in the detailed analysis of neural structures by allowing efficient navigation and annotation of 3D datasets.[41] Neuroglancer, a web-based tool designed for visualizing and navigating large-scale neuroscience data, offers features like 3D rendering and interactive exploration of brain datasets.

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).

Comparative connectomics

edit

Comparative connectomics is a subfield in neuroscience that focuses on comparing the connectomes, or neural network maps, across different species, developmental stages, or pathological states.[42] This comparative approach aims to uncover fundamental principles of brain organization and function by identifying conserved and divergent patterns in neural circuitry. By analyzing similarities and differences in the wiring diagrams of various organisms, researchers can gain insights into the evolutionary processes shaping the nervous system, as well as into the neural basis of behavior and cognition. For example, a 2022 study comparing synaptic connectivity in the mouse and human/macaque cortex revealed that, even though the human cortex contains three times more interneurons than the mouse cortex, the excitation-to-inhibition ratio is similar between the species.[30]

Model systems

edit

Aside from the human brain, some of the model systems used for connectomics research are the mouse,[43] the fruit fly,[44][45] the nematode C. elegans,[46][47] and the barn owl.[48]

Caenorhabditis Elegans

edit

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;[49] This process involved studying the aging process of the C. elegans brain by comparing varying worms from birth to adulthood.[50] Researchers found the biggest change with age is the wiring circuits, and that connectivity between and within brain regions increases with age.[50] 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.[50]

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.[51] Overall, the experiment investigates the connection between neuroanatomy and behavior given that there is a lot of available information about the worm already discovered.[51]

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.[52] 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.[53]

Fruit Fly

edit

Within the last decade, largely owing to technological advancements in EM data collection and image processing, multiple synapse-scale connectome datasets have been generated for the fruit fly Drosophila melanogaster in its adult and larval forms.

The largest current dataset is the FlyWire segmentation and annotation of the female adult fly brain (FAFB) volume,[24] which encompasses the entire brain of an adult. The FAFB volume was imaged by a team at Janelia Research Campus using a novel high-throughput serial section transmission electron microscopy (ssTEM) pipeline.[45] Dr. Sebastian Seung’s lab at Princeton used convolutional neural networks (CNNs) to automatically segment neurons and detect pre- and post-synaptic sites in the volume. This automated version was then used as a starting point for a massive community effort among fly neuroscientists to proofread neuronal morphologies by correcting errors and adding information about cell type and other attributes.[54] This effort was conducted by FlyWire, conducted by Dr. Sebastian Seung and Dr. Mala Murthy (also at the Princeton Neuroscience Institute), in conjunction with a large team of other scientists and labs called the FlyWire Consortium.[54][55] The full brain connectome produced by this effort is now publicly available and searchable through the FlyWire Codex.[56][57]

Another adult brain dataset available is the Hemibrain, generated as a collaboration between the Janelia FlyEM team and Google.[58][59] This dataset is an incomplete but large section of the fly central brain. It was collected using focused ion beam scanning electron microscopy (FIB-SEM) which generated an 8 nm isotropic dataset, then automatically segmented using a flood-filling network before being manually proofread by a team of experts. This dataset is also publicly available and searchable on a platform called neuPrint.[60] Members of the fly connectomics community have made an effort to match cell types between FlyWire and the Hemibrain. They have found that at first pass, 61% of Hemibrain types are found in the FlyWire dataset and, out of these consensus cell types, 53% of “edges” from one cell type to another can be found in both datasets (but edges connected by at least 10 synapses are much more consistently found across datasets).[61]

There are also currently two publicly available datasets of the adult fly ventral nerve cord (VNC). The female adult nerve cord (FANC) was collected using high-throughput ssTEM by Dr. Wei-Chung Allen Lee’s lab at Harvard Medical School.[4] It then underwent automatic segmentation and synapse prediction using CNNs, and researchers at Harvard and the University of Washington mapped motor neurons with cell bodies in the VNC to their muscular targets by cross-referencing between the EM dataset, a high-resolution nanotomography image volume of the fly leg, and sparse genetic lines to label individual neurons with fluorescent proteins.[62] The FANC dataset is currently partially proofread and annotated. The male adult nerve cord (MANC) was collected and segmented at Janelia using FIB-SEM and flood-filling network protocols modified from the Hemibrain pipeline.[26] In a collaboration between researchers at Janelia, Google, the University of Cambridge, and the MRC Laboratory of Molecular Biology (LMB), it is fully proofread and annotated with cell types and other properties, and searchable on neuPrint.[63]

The connectome of a complete central nervous system (connected brain and VNC) of a 1st instar D. melanogaster larva has been collected as a single volume. This was once again undertaken at Janelia using FIB-SEM, similarly to the Hemibrain data collection.[27] This dataset of 3016 neurons was segmented and annotated manually using CATMAID by a team of people mainly led by researchers at Janelia, Cambridge, and the MRC LMB.[27]

Mouse

edit

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.[64] 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.[65] 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 the reconstruction of 100,000+ neurons and the imaging technology is advanced enough to do so.[65]

Mice models in the lab have provided insight into genetic brain disorders, one study manipulated mice with a deletion of 22q11.2 (chromosome 22, a likely known genetic risk factor that leads to schizophrenia).[66] The findings of this study showed that this impaired neural activity in mice's working memory is similar to what it does in humans.[66]

Leading Connectomics Labs

edit

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, zebrafish, and C. elegans.[67] 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 zebrafish.[68] 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.[65]

Another notable lab is the Lee Lab of Harvard Medical School which 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".[69]

Applications

edit
 
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.[70][self-published source?] Current neural networks mostly rely on probabilistic representations of connectivity patterns.[71] Connectivity matrices (checkerboard diagrams of connectomics) have been used in stroke recovery to evaluate the response to treatment via Transcranial Magnetic Stimulation.[72] Similarly, connectograms (circular diagrams of connectomics) have been used in traumatic brain injury cases to document the extent of damage to neural networks.[73][74]

Looking into these methods of research, they can reveal information about different mental illnesses and brain disorders. The tracking of brain networks in alignment with diseases and illnesses would be enhanced by these advanced technologies that can produce complex images of neural networks.[75] With this in mind, diseases can not only be tracked, but predicted based on behavior of previous cases, a process that would take an extensive period of time to collect and record.[75] Specifically, studies on different brain disorders such as schizophrenia and bipolar disorder with a focus on the connectomics involved reveal information. Both of these disorders have a similar genetic origin,[75][76] and research found that those with higher polygenic scores for schizophrenia and bipolar disorder have lower amounts of connectivity shown through neuroimaging.[77] This method of research tackles real-world applications of connectomics, combining methods of imaging with genetics to dig deeper into the origins and outcomes of genetically related disorders.[75]  Another study supports the finding that there is relation between connectivity and likelihood of disease, as researchers found those diagnosed with schizophrenia have less structurally complete brain networks.[78] The main drawback in this area of connectomics is not being able to achieve images of whole-brain networks, therefore it is hard to make complete and accurate assumptions about cause and effect of diseases' neural pathways.[78] Connectomics has been used to study patients with strokes using MRI imaging, however because such little research is done in this specific area, conclusions cannot be drawn regarding the relation between strokes and connectivity.[79] The research did find results that highlight an association between poor connectivity in the language system and poor motor coordination, however the results were not substantial enough to make a bold claim.[79] For behavioral disorders, it can be difficult to diagnose and treat because most situations revolve on a symptoms-based approach. However, this can be difficult because many disorders have overlapping symptoms. Connectomics has been used to find neuromarkers associated with social anxiety disorder (SAD) at a high precision rate in improving related symptoms.[80] This is an expanding field and there is room for greater application to mental health disorders and brain malfunction, in which current research is building on neural networks and the psychopathology involved.[81]

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.[82][83] 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).[84] Those can be found by using either an adjusted t-test,[85] or a sparsity model,[84] 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.[86] 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

edit

The recent advancements in the field of connectomics have 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.[87] 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.[88] 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 to study their impact on neural circuitry and behavior.[87] 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.[89] 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.[90]

Human Connectome Project

edit

The Human Connectome Project (HCP) is an initiative launched in 2009 by the National Institutes of Health (NIH) to map the neural pathways that underlie human brain function.[91] The goal was to obtain and distribute information regarding the structural and functional connections within the human brain, improving imaging and analysis methods to enhance resolution and practicality in the realm of connectomics.[91] 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.[91] The Connectome Coordination Facility serves as a centralized repository for HCP data and provides support to researchers.[91] 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.[92] The project would aim to utilize recent advancements in visualization technologies towards a higher spatial resolution in imaging structural connectivity.[92] 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.[92]

Eyewire game

edit
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.

Public datasets

edit

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

edit

References

edit
  1. ^ Quartarone A, Cacciola A, Milardi D, Ghilardi MF, Calamuneri A, Chillemi G, et al. (February 2020). "New insights into cortico-basal-cerebellar connectome: clinical and physiological considerations". Brain. 143 (2): 396–406. doi:10.1093/brain/awz310. PMID 31628799.
  2. ^ Nguyen TM, Thomas LA, Rhoades JL, Ricchi I, Yuan XC, Sheridan A, et al. (2023-01-19). "Structured cerebellar connectivity supports resilient pattern separation". Nature. 613 (7944): 543–549. Bibcode:2023Natur.613..543N. bioRxiv 10.1101/2021.11.29.470455. doi:10.1038/s41586-022-05471-w. ISSN 0028-0836. PMC 10324966. PMID 36418404.
  3. ^ Helmstaedter M, Briggman KL, Turaga SC, Jain V, Seung HS, Denk W (August 2013). "Connectomic reconstruction of the inner plexiform layer in the mouse retina". Nature. 500 (7461): 168–174. Bibcode:2013Natur.500..168H. doi:10.1038/nature12346. PMID 23925239. S2CID 3119909.
  4. ^ a b Phelps JS, Hildebrand DG, Graham BJ, Kuan AT, Thomas LA, Nguyen TM, et al. (February 2021). "Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy". Cell. 184 (3): 759–774.e18. doi:10.1016/j.cell.2020.12.013. PMC 8312698. PMID 33400916.
  5. ^ Boonstra TW, Danna-Dos-Santos A, Xie HB, Roerdink M, Stins JF, Breakspear M (December 2015). "Muscle networks: Connectivity analysis of EMG activity during postural control". Scientific Reports. 5: 17830. Bibcode:2015NatSR...517830B. doi:10.1038/srep17830. PMC 4669476. PMID 26634293.
  6. ^ a b c d Sotiropoulous S, Zalesky A (June 27, 2017). "Building connectomes using diffusion MRI: why, how and but". NMR in Biomedicine. 32 (4): e3752. doi:10.1002/nbm.3752. PMC 6491971. PMID 28654718.
  7. ^ "Figure 7. Strong long-range connections can delocalize a subset of eigenvectors". doi:10.7554/elife.01239.010. {{cite journal}}: Cite journal requires |journal= (help)
  8. ^ a b Deligianni F, Centeno M, Carmichael DW, Clayden JD (2014). "Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands". Frontiers in Neuroscience. 8: 258. doi:10.3389/fnins.2014.00258. ISSN 1662-453X. PMC 4148011. PMID 25221467.
  9. ^ Zhu F, Tang L (July 5, 2019). "Resting-state functional magnetic resonance imaging (fMRI) and functional connectivity density mapping in patients with corneal ulcer". Neuropsychiatric Disease and Treatment. 15: 1833–1844. doi:10.2147/NDT.S210658. PMC 6617566. PMID 31308676.
  10. ^ Horn A, Fox MD (2020-11-01). "Opportunities of connectomic neuromodulation". NeuroImage. 221: 117180. doi:10.1016/j.neuroimage.2020.117180. ISSN 1053-8119. PMC 7847552. PMID 32702488.
  11. ^ a b Xia M, He Y (2022-10-01). "Connectome-guided transcranial magnetic stimulation treatment in depression". European Child & Adolescent Psychiatry. 31 (10): 1481–1483. doi:10.1007/s00787-022-02089-1. ISSN 1435-165X. PMID 36151354. S2CID 252497452.
  12. ^ a b c Kunze T, Hunold A, Haueisen J, Jirsa V, Spiegler A (2016-10-15). "Transcranial direct current stimulation changes resting state functional connectivity: A large-scale brain network modeling study". NeuroImage. Transcranial electric stimulation (tES) and Neuroimaging. 140: 174–187. doi:10.1016/j.neuroimage.2016.02.015. hdl:11858/00-001M-0000-002C-EBE2-7. ISSN 1053-8119. PMID 26883068. S2CID 17716820.
  13. ^ a b Wang Q, Akram H, Muthuraman M, Gonzalez-Escamilla G, Sheth SA, Oxenford S, et al. (2021-01-01). "Normative vs. patient-specific brain connectivity in deep brain stimulation". NeuroImage. 224: 117307. doi:10.1016/j.neuroimage.2020.117307. ISSN 1053-8119. PMID 32861787. S2CID 216357803.
  14. ^ Benabid AL (2003-12-01). "Deep brain stimulation for Parkinson's disease". Current Opinion in Neurobiology. 13 (6): 696–706. doi:10.1016/j.conb.2003.11.001. ISSN 0959-4388. PMID 14662371. S2CID 27174469.
  15. ^ Sadaghiani S, Brookes MJ, Baillet S (2022-02-15). "Connectomics of human electrophysiology". NeuroImage. 247: 118788. doi:10.1016/j.neuroimage.2021.118788. ISSN 1053-8119. PMC 8943906. PMID 34906715.
  16. ^ a b Sadaghiani S, Brookes MJ, Baillet S (2022-02-15). "Connectomics of human electrophysiology". NeuroImage. 247: 118788. doi:10.1016/j.neuroimage.2021.118788. ISSN 1053-8119. PMC 8943906. PMID 34906715.
  17. ^ Kammen A, Law M, Tjan BS, Toga AW, Shi Y (January 2016). "Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis". NeuroImage. 125: 767–779. doi:10.1016/j.neuroimage.2015.11.005. PMC 4691391. PMID 26551261.
  18. ^ Yogarajah M, Focke NK, Bonelli S, Cercignani M, Acheson J, Parker GJ, et al. (June 2009). "Defining Meyer's loop-temporal lobe resections, visual field deficits and diffusion tensor tractography". Brain. 132 (Pt 6): 1656–1668. doi:10.1093/brain/awp114. PMC 2685925. PMID 19460796.
  19. ^ Nieuwenhuys R, Voogd J, Van Huijzen C (2008). The Human Central Nervous System. doi:10.1007/978-3-540-34686-9. ISBN 978-3-540-34684-5.
  20. ^ Paxinos G, Huang XF, Sengul G, Watson C (2012). "Organization of Brainstem Nuclei". The Human Nervous System. Elsevier. pp. 260–327. doi:10.1016/b978-0-12-374236-0.10008-2. ISBN 978-0-12-374236-0.
  21. ^ Glasser MF, Rilling JK (November 2008). "DTI tractography of the human brain's language pathways". Cerebral Cortex. 18 (11): 2471–2482. doi:10.1093/cercor/bhn011. PMID 18281301.
  22. ^ Catani M, Jones DK, ffytche DH (January 2005). "Perisylvian language networks of the human brain". Annals of Neurology. 57 (1): 8–16. doi:10.1002/ana.20319. PMID 15597383. S2CID 17743067.
  23. ^ a b White JG, Southgate E, Thomson JN, Brenner S (1986-11-12). "The structure of the nervous system of the nematode Caenorhabditis elegans". Philosophical Transactions of the Royal Society of London. B, Biological Sciences. 314 (1165): 1–340. Bibcode:1986RSPTB.314....1W. doi:10.1098/rstb.1986.0056. ISSN 0080-4622. PMID 22462104.
  24. ^ a b Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu Sc, McKellar CE, et al. (2023-06-30). Neuronal wiring diagram of an adult brain (Report). Neuroscience. doi:10.1101/2023.06.27.546656. PMC 10327113. PMID 37425937.
  25. ^ Phelps JS, Hildebrand DG, Graham BJ, Kuan AT, Thomas LA, Nguyen TM, et al. (February 2021). "Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy". Cell. 184 (3): 759–774.e18. doi:10.1016/j.cell.2020.12.013. ISSN 0092-8674. PMC 8312698. PMID 33400916.
  26. ^ a b Takemura Sy, Hayworth KJ, Huang GB, Januszewski M, Lu Z, Marin EC, et al. (2023-06-06). A Connectome of the Male Drosophila Ventral Nerve Cord (Report). Neuroscience. doi:10.1101/2023.06.05.543757.
  27. ^ a b c Winding M, Pedigo BD, Barnes CL, Patsolic HG, Park Y, Kazimiers T, et al. (2023-03-10). "The connectome of an insect brain". Science. 379 (6636): eadd9330. doi:10.1126/science.add9330. ISSN 0036-8075. PMC 7614541. PMID 36893230.
  28. ^ The MICrONS Consortium, Bae JA, Baptiste M, Bishop CA, Bodor AL, Brittain D, et al. (2021-07-29). Functional connectomics spanning multiple areas of mouse visual cortex (Report). Neuroscience. doi:10.1101/2021.07.28.454025.
  29. ^ Shapson-Coe A, Januszewski M, Berger DR, Pope A, Wu Y, Blakely T, et al. (2021-05-30). A connectomic study of a petascale fragment of human cerebral cortex (Report). Neuroscience. doi:10.1101/2021.05.29.446289.
  30. ^ a b Loomba S, Straehle J, Gangadharan V, Heike N, Khalifa A, Motta A, et al. (2022-07-08). "Connectomic comparison of mouse and human cortex". Science. 377 (6602): eabo0924. doi:10.1126/science.abo0924. ISSN 0036-8075. PMID 35737810.
  31. ^ "BRAIN CONNECTS: A Center for High-throughput Integrative Mouse Connectomics". NIH RePORTER. Retrieved October 17, 2023.
  32. ^ a b Kuan AT, Phelps JS, Thomas LA, Nguyen TM, Han J, Chen CL, et al. (December 2020). "Dense neuronal reconstruction through X-ray holographic nano-tomography". Nature Neuroscience. 23 (12): 1637–1643. doi:10.1038/s41593-020-0704-9. ISSN 1097-6256. PMC 8354006. PMID 32929244.
  33. ^ a b c Velicky P, Miguel E, Michalska JM, Lyudchik J, Wei D, Lin Z, et al. (August 2023). "Dense 4D nanoscale reconstruction of living brain tissue". Nature Methods. 20 (8): 1256–1265. doi:10.1038/s41592-023-01936-6. ISSN 1548-7091. PMC 10406607. PMID 37429995.
  34. ^ Wedeen VJ, Wang RP, Schmahmann JD, Benner T, Tseng WY, Dai G, et al. (July 2008). "Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers". NeuroImage. 41 (4): 1267–1277. doi:10.1016/j.neuroimage.2008.03.036. PMID 18495497. S2CID 2660208.
  35. ^ Damoiseaux JS, Greicius MD (October 2009). "Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity". Brain Structure & Function. 213 (6): 525–533. doi:10.1007/s00429-009-0208-6. PMID 19565262. S2CID 16792748.
  36. ^ Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, et al. (February 2009). "Predicting human resting-state functional connectivity from structural connectivity". Proceedings of the National Academy of Sciences of the United States of America. 106 (6): 2035–2040. Bibcode:2009PNAS..106.2035H. doi:10.1073/pnas.0811168106. PMC 2634800. PMID 19188601.
  37. ^ Anderson JR, Jones BW, Watt CB, Shaw MV, Yang JH, Demill D, et al. (February 2011). "Exploring the retinal connectome". Molecular Vision. 17: 355–379. PMC 3036568. PMID 21311605.
  38. ^ BV D. "Neuroscience: Synaptic connectivity in the songbird brain - Application Note | DELMIC". request.delmic.com. Retrieved 2017-02-16.
  39. ^ Motta A, Berning M, Boergens KM, Staffler B, Beining M, Loomba S, et al. (2019-11-29). "Dense connectomic reconstruction in layer 4 of the somatosensory cortex". Science. 366 (6469). doi:10.1126/science.aay3134. ISSN 0036-8075. PMID 31649140.
  40. ^ Saalfeld S, Cardona A, Hartenstein V, Tomančák P (2009-04-17). "CATMAID: collaborative annotation toolkit for massive amounts of image data". Bioinformatics. 25 (15): 1984–1986. doi:10.1093/bioinformatics/btp266. ISSN 1367-4811. PMC 2712332. PMID 19376822.
  41. ^ Boergens KM, Berning M, Bocklisch T, Bräunlein D, Drawitsch F, Frohnhofen J, et al. (July 2017). "webKnossos: efficient online 3D data annotation for connectomics". Nature Methods. 14 (7): 691–694. doi:10.1038/nmeth.4331. ISSN 1548-7105. PMID 28604722. S2CID 30609228.
  42. ^ van den Heuvel MP, Bullmore ET, Sporns O (May 2016). "Comparative Connectomics". Trends in Cognitive Sciences. 20 (5): 345–361. doi:10.1016/j.tics.2016.03.001. ISSN 1364-6613. PMID 27026480. S2CID 3629442.
  43. ^ Bock DD, Lee WC, Kerlin AM, Andermann ML, Hood G, Wetzel AW, et al. (March 2011). "Network anatomy and in vivo physiology of visual cortical neurons". Nature. 471 (7337): 177–182. Bibcode:2011Natur.471..177B. doi:10.1038/nature09802. PMC 3095821. PMID 21390124.
  44. ^ Chklovskii DB, Vitaladevuni S, Scheffer LK (October 2010). "Semi-automated reconstruction of neural circuits using electron microscopy". Current Opinion in Neurobiology. 20 (5): 667–675. doi:10.1016/j.conb.2010.08.002. PMID 20833533. S2CID 206950616.
  45. ^ a b Zheng Z, Lauritzen JS, Perlman E, Robinson CG, Nichols M, Milkie D, et al. (July 2018). "A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster". Cell. 174 (3): 730–743.e22. doi:10.1016/j.cell.2018.06.019. PMC 6063995. PMID 30033368.
  46. ^ Chen BL, Hall DH, Chklovskii DB (March 2006). "Wiring optimization can relate neuronal structure and function". Proceedings of the National Academy of Sciences of the United States of America. 103 (12): 4723–4728. Bibcode:2006PNAS..103.4723C. doi:10.1073/pnas.0506806103. PMC 1550972. PMID 16537428.
  47. ^ Pérez-Escudero A, Rivera-Alba M, de Polavieja GG (December 2009). "Structure of deviations from optimality in biological systems". Proceedings of the National Academy of Sciences of the United States of America. 106 (48): 20544–20549. Bibcode:2009PNAS..10620544P. doi:10.1073/pnas.0905336106. PMC 2777958. PMID 19918070.
  48. ^ Pena JL, DeBello WM (2010). "Auditory processing, plasticity, and learning in the barn owl". ILAR Journal. 51 (4): 338–352. doi:10.1093/ilar.51.4.338. PMC 3102523. PMID 21131711.
  49. ^ "WormWiring". www.wormwiring.org. Retrieved 2023-06-19.
  50. ^ a b c Witvliet D, Mulcahy B, Mitchell JK, Meirovitch Y, Berger DR, Wu Y, et al. (August 2021). "Connectomes across development reveal principles of brain maturation". Nature. 596 (7871): 257–261. Bibcode:2021Natur.596..257W. doi:10.1038/s41586-021-03778-8. PMC 8756380. PMID 34349261.
  51. ^ a b Izquierdo EJ, Beer RD (2013-02-07). "Connecting a connectome to behavior: an ensemble of neuroanatomical models of C. elegans klinotaxis". PLOS Computational Biology. 9 (2): e1002890. Bibcode:2013PLSCB...9E2890I. doi:10.1371/journal.pcbi.1002890. PMC 3567170. PMID 23408877.
  52. ^ "Overview". medicine.yale.edu. Retrieved 2023-06-19.
  53. ^ "What is Connectomics?". News-Medical.net. 2019-04-11. Retrieved 2023-06-19.
  54. ^ a b Dorkenwald S, McKellar CE, Macrina T, Kemnitz N, Lee K, Lu R, et al. (January 2022). "FlyWire: online community for whole-brain connectomics". Nature Methods. 19 (1): 119–128. doi:10.1038/s41592-021-01330-0. ISSN 1548-7105. PMC 8903166. PMID 34949809.
  55. ^ "FlyWire". flywire.ai. Retrieved 2024-02-22.
  56. ^ "Codex". codex.flywire.ai. Retrieved 2024-02-22.
  57. ^ Matsliah A, Sterling AR, Dorkenwald S, Kuehner K, Morey R, H Sebastian Seung, et al. (2023). "Codex: Connectome Data Explorer". doi:10.13140/RG.2.2.35928.67844. {{cite journal}}: Cite journal requires |journal= (help)
  58. ^ Scheffer LK, Xu CS, Januszewski M, Lu Z, Takemura Sy, Hayworth KJ, et al. (2020-09-03). Marder E, Eisen MB, Pipkin J, Doe CQ (eds.). "A connectome and analysis of the adult Drosophila central brain". eLife. 9: e57443. doi:10.7554/eLife.57443. ISSN 2050-084X. PMC 7546738. PMID 32880371.
  59. ^ "Hemibrain". Janelia Research Campus. Retrieved 2024-02-22.
  60. ^ "neuPrintExplorer". neuprint.janelia.org. Retrieved 2024-02-22.
  61. ^ Schlegel P, Yin Y, Bates AS, Dorkenwald S, Eichler K, Brooks P, et al. (2023-07-15), "Whole-brain annotation and multi-connectome cell typing quantifies circuit stereotypy in Drosophila", BioRxiv: The Preprint Server for Biology, doi:10.1101/2023.06.27.546055, PMC 10327018, PMID 37425808, retrieved 2024-02-22
  62. ^ Azevedo A, Lesser E, Mark B, Phelps J, Elabbady L, Kuroda S, et al. (2022-12-15), Tools for comprehensive reconstruction and analysis of Drosophila motor circuits, doi:10.1101/2022.12.15.520299, S2CID 254736092, retrieved 2024-02-22
  63. ^ "neuPrintExplorer". neuprint.janelia.org. Retrieved 2024-02-22.
  64. ^ "MouseLight". www.mouselight.janelia.org. Retrieved 2023-06-19.
  65. ^ a b c Winnubst J, Bas E, Ferreira TA, Wu Z, Economo MN, Edson P, et al. (September 2019). "Reconstruction of 1,000 Projection Neurons Reveals New Cell Types and Organization of Long-Range Connectivity in the Mouse Brain". Cell. 179 (1): 268–281.e13. doi:10.1016/j.cell.2019.07.042. PMC 6754285. PMID 31495573.
  66. ^ a b Sigurdsson T, Stark KL, Karayiorgou M, Gogos JA, Gordon JA (April 2010). "Impaired hippocampal-prefrontal synchrony in a genetic mouse model of schizophrenia". Nature. 464 (7289): 763–767. Bibcode:2010Natur.464..763S. doi:10.1038/nature08855. PMC 2864584. PMID 20360742.
  67. ^ "Research". lichtmanlab.fas.harvard.edu. Retrieved 2023-06-19.
  68. ^ Pan YA, Freundlich T, Weissman TA, Schoppik D, Wang XC, Zimmerman S, et al. (July 2013). "Zebrabow: multispectral cell labeling for cell tracing and lineage analysis in zebrafish". Development. 140 (13): 2835–2846. doi:10.1242/dev.094631. PMC 3678346. PMID 23757414.
  69. ^ "Research". Lee Lab | Wei-Chung Allen Lee, PhD. Retrieved 2023-06-19.
  70. ^ http://www.scholarpedia.org/article/Connectome[unreliable medical source?][permanent dead link‍]
  71. ^ Nordlie E, Gewaltig MO, Plesser HE (August 2009). Friston KJ (ed.). "Towards reproducible descriptions of neuronal network models". PLOS Computational Biology. 5 (8): e1000456. Bibcode:2009PLSCB...5E0456N. doi:10.1371/journal.pcbi.1000456. PMC 2713426. PMID 19662159.
  72. ^ Yeung JT, Young IM, Doyen S, Teo C, Sughrue ME (October 2021). "Changes in the Brain Connectome Following Repetitive Transcranial Magnetic Stimulation for Stroke Rehabilitation". Cureus. 13 (10): e19105. doi:10.7759/cureus.19105. PMC 8614179. PMID 34858752.
  73. ^ Van Horn JD, Irimia A, Torgerson CM, Chambers MC, Kikinis R, Toga AW (2012). "Mapping connectivity damage in the case of Phineas Gage". PLOS ONE. 7 (5): e37454. Bibcode:2012PLoSO...737454V. doi:10.1371/journal.pone.0037454. PMC 3353935. PMID 22616011.
  74. ^ Irimia A, Chambers MC, Torgerson CM, Filippou M, Hovda DA, Alger JR, et al. (6 February 2012). "Patient-tailored connectomics visualization for the assessment of white matter atrophy in traumatic brain injury". Frontiers in Neurology. 3: 10. doi:10.3389/fneur.2012.00010. PMC 3275792. PMID 22363313.
  75. ^ a b c d Fornito A, Zalesky A, Breakspear M (March 2015). "The connectomics of brain disorders". Nature Reviews Neuroscience. 16 (3): 159–172. doi:10.1038/nrn3901. ISSN 1471-0048. PMID 25697159. S2CID 1792111.
  76. ^ Stahl EA, Breen G, Forstner AJ, McQuillin A, Ripke S, Trubetskoy V, et al. (May 2019). "Genome-wide association study identifies 30 loci associated with bipolar disorder". Nature Genetics. 51 (5): 793–803. doi:10.1038/s41588-019-0397-8. hdl:10481/58017. ISSN 1546-1718. PMC 6956732. PMID 31043756.
  77. ^ Wei Y, De Lange SC, Savage JE, Tissink E, Qi T, Repple J, et al. (2022-11-09). "Associated Genetics and Connectomic Circuitry in Schizophrenia and Bipolar Disorder". Biological Psychiatry. 94 (2): 174–183. doi:10.1016/j.biopsych.2022.11.006. ISSN 0006-3223. PMID 36803976. S2CID 253426641.
  78. ^ a b Heuvel MP, Mandl RC, Stam CJ, Kahn RS, Pol HE (2010-11-24). "Aberrant Frontal and Temporal Complex Network Structure in Schizophrenia: A Graph Theoretical Analysis". Journal of Neuroscience. 30 (47): 15915–15926. doi:10.1523/JNEUROSCI.2874-10.2010. ISSN 0270-6474. PMC 6633761. PMID 21106830.
  79. ^ a b Bian R, Huo M, Liu W, Mansouri N, Tanglay O, Young I, et al. (2023). "Connectomics underlying motor functional outcomes in the acute period following stroke". Frontiers in Aging Neuroscience. 15. doi:10.3389/fnagi.2023.1131415. ISSN 1663-4365. PMC 9975347. PMID 36875697.
  80. ^ Whitfield-Gabrieli S, Ghosh SS, Nieto-Castanon A, Saygin Z, Doehrmann O, Chai XJ, et al. (May 2016). "Brain connectomics predict response to treatment in social anxiety disorder". Molecular Psychiatry. 21 (5): 680–685. doi:10.1038/mp.2015.109. ISSN 1476-5578. PMID 26260493. S2CID 1654492.
  81. ^ Menon V (2011-10-01). "Large-scale brain networks and psychopathology: a unifying triple network model". Trends in Cognitive Sciences. 15 (10): 483–506. doi:10.1016/j.tics.2011.08.003. ISSN 1364-6613. PMID 21908230. S2CID 26653572.
  82. ^ Szalkai B, Varga B, Grolmusz V (2015). "Graph Theoretical Analysis Reveals: Women's Brains Are Better Connected than Men's". PLOS ONE. 10 (7): e0130045. arXiv:1501.00727. Bibcode:2015PLoSO..1030045S. doi:10.1371/journal.pone.0130045. PMC 4488527. PMID 26132764.
  83. ^ Szalkai B, Varga B, Grolmusz V (June 2018). "Brain size bias compensated graph-theoretical parameters are also better in women's structural connectomes". Brain Imaging and Behavior. 12 (3): 663–673. doi:10.1007/s11682-017-9720-0. PMID 28447246. S2CID 4028467.
  84. ^ a b Crimi A, Giancardo L, Sambataro F, Gozzi A, Murino V, Sona D (January 2019). "MultiLink Analysis: Brain Network Comparison via Sparse Connectivity Analysis". Scientific Reports. 9 (1): 65. Bibcode:2019NatSR...9...65C. doi:10.1038/s41598-018-37300-4. PMC 6329758. PMID 30635604.
  85. ^ Zalesky A, Fornito A, Bullmore ET (December 2010). "Network-based statistic: identifying differences in brain networks". NeuroImage. 53 (4): 1197–1207. doi:10.1016/j.neuroimage.2010.06.041. PMID 20600983. S2CID 17760084.
  86. ^ Kerepesi C, Szalkai B, Varga B, Grolmusz V (January 2018). "Comparative connectomics: Mapping the inter-individual variability of connections within the regions of the human brain". Neuroscience Letters. 662 (1): 17–21. arXiv:1507.00327. doi:10.1016/j.neulet.2017.10.003. PMID 28988973. S2CID 378080.
  87. ^ a b Chen PB, Flint J (July 2021). "What connectomics can learn from genomics". PLOS Genetics. 17 (7): e1009692. doi:10.1371/journal.pgen.1009692. PMC 8318269. PMID 34270560.
  88. ^ Arnatkeviciute A, Fulcher BD, Bellgrove MA, Fornito A (December 2021). "Where the genome meets the connectome: Understanding how genes shape human brain connectivity". NeuroImage. 244: 118570. doi:10.1016/j.neuroimage.2021.118570. PMID 34508898. S2CID 237448401.
  89. ^ Lichtman JW, Sanes JR (June 2008). "Ome sweet ome: what can the genome tell us about the connectome?". Current Opinion in Neurobiology. 18 (3): 346–353. doi:10.1016/j.conb.2008.08.010. PMC 2735215. PMID 18801435.
  90. ^ Vance A (27 December 2010). "Seeking the Connectome, a Mental Map, Slice by Slice". The New York Times.
  91. ^ a b c d "Connectome Programs | Blueprint". neuroscienceblueprint.nih.gov. Retrieved 2023-06-19.
  92. ^ a b c Elam JS, Glasser MF, Harms MP, Sotiropoulos SN, Andersson JL, Burgess GC, et al. (December 2021). "The Human Connectome Project: A retrospective". NeuroImage. 244: 118543. doi:10.1016/j.neuroimage.2021.118543. PMC 9387634. PMID 34508893.

Further reading

edit
edit
Retrieved from "https://en.wikipedia.org/w/index.php?title=Connectomics&oldid=1258249293"