The Human Connectome: A Structural Description of the Human Brain
Open accessAbstract
The connection matrix of the human brain (the human "connectome") represents an indispensable foundation for basic and applied neurobiological research. However, the network of anatomical connections linking the neuronal elements of the human brain is still largely unknown. While some databases or collations of large-scale anatomical connection patterns exist for other mammalian species, there is currently no connection matrix of the human brain, nor is there a coordinated research effort to collect, archive, and disseminate this important information. We propose a research strategy to achieve this goal, and discuss its potential impact.
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A recent focus on brain connectivity as the underlying principle of the brain has gained eminence in the past decade, culminating in the search for the connectome, or the brain's wiring diagram.
“While a network approach to brain function is not a new idea, 25,49,68,85,88 a recent focus on brain connectivity as the underlying principle of the brain has gained eminence in the past decade, culminating in the search for the connectome, or the brain's “wiring diagram.”89”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [89]
The Introduction proposes the human “connectome” as a structural description of the brain’s elements and connections, and describes emerging network theories of cognition. It does not establish that connectivity gained prominence over the preceding decade, so the claim’s timeline is not supported by this source. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe anatomical accuracy of diffusion-weighted tractography continues to be a matter of controversy, and reconstructing connectomes with both high sensitivity and high specificity remains challenging.
“However, the anatomical accuracy of diffusion-weighted tractography, which infers neuronal pathways from tissue water diffusion direction, continues to be a matter of controversy, and reconstructing connectomes with both high sensitivity and high specificity remains challenging (Sporns et al. 2005; Bastiani et al. 2012; Jbabdi et al. 2013, 2015; Thomas et al. 2014; Azadbakht et al. 2015; Calabrese”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Sporns et al. 2005
In “Macroscale: Brain Regions and Pathways” (page unavailable), the paper says the relationship between diffusion-imaging results, fiber tracts, and neuronal connections remains controversial, and it describes limitations in acquisition and processing. It also says no single mapping method is likely to be sufficiently comprehensive. It does not specifically address the challenge of achieving both high sensitivity and high specificity in reconstructed connectomes. (LLM, conf=0.86)
Experimental — accuracy not yet measuredQuantitative measures extracted for individual fiber pathways can be used to construct a structural connectivity matrix mapping the whole-brain network of structural connectivity between all pairs of gray matter regions.
“Quantitative measures extracted for individual fiber pathways can also be used to construct a structural connectivity matrix to map the whole-brain network of structural connectivity between all pairs of gray matter regions (Hagmann et al., 2008a; Sporns et al., 2005).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Sporns et al., 2005
In “Steps Toward the Human Connectome,” the paper proposes using probabilistic tractography of fiber pathways to produce an all-to-all structural connectivity matrix for the human brain. It specifies a voxel-wise probabilistic matrix, however, rather than a matrix between all pairs of gray-matter regions, and does not specify quantitative measures extracted for each individual pathway. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMedical neuralnanorobots are expected to have the capacity for real-time, non-destructive monitoring of single-neuron and single-synapse neuroelectric activity, local neuropeptide traffic, and other relevant functional data, while also allowing the acquisition of fundamental structural information from neuron surfaces, to enhance the connectome map of a living human brain.
“Medical neuralnanorobots are expected to have the capacity for real-time, non-destructive monitoring of single-neuron and single-synapse neuroelectric activity, local neuropeptide traffic, and other relevant functional data, while also allowing the acquisition of fundamental structural information from neuron surfaces, to enhance the connectome map of a living human brain”
From Human Brain/Cloud Interface (2019) · cites this paper as Sporns et al., 2005
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“Substantial, albeit smaller, initiatives have also made data available to the general community such as The Human Connectome [20], UK Biobank [21], The Cancer Imaging Archive (TCIA) [22], NIH CXR8 [23], NIH DeepLesion [24], The Cancer Genome Atlas (TCGA) [25], the Alzheimer’s disease neu-”
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“The full wiring diagram of the brain, in all its complexity, is often called the connectome.(19,20) Capturing the full connectome can be regarded as the holy grail of fiber tractography (and perhaps even of the whole of neuroscience for that matter), as it will provide us with unprecedented insight into the brain's ar…”
BackgroundNeuronal wiring diagram of an adult brain
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“Such maps have been called projectomes 79 or mesoscale connectomes 14 .”
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Small worlds inside big brains
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“tural connectivity of the entire brain [429, 527, 211, 34].”
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Connectome sensitivity or specificity: which is more important?
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Breathing as a fundamental rhythm of brain function.
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The human connectome in Alzheimer disease — relationship to biomarkers and genetics
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The development of hub architecture in the human functional brain network
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Development of Human Brain Structural Networks Through Infancy and Childhood
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Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory
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Architecture of the cerebral cortical association connectome underlying cognition
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Mapping Structural Connectivity Using Diffusion <scp>MRI</scp> : Challenges and Opportunities
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Neuronal wiring diagram of an adult brain
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Self-citation
- Authors
- Sporns, O., Tononi, G., & Kötter, R.
- Affiliations
- Indiana University Bloomington (US)
- University of Wisconsin–Madison (US)
- Corresponding author
- Sporns, Olaf
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2005
- Volume
- 1
- Issue
- 4
- Pages
- e42
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC1239902
- OpenAlex
- W2146693559
- Cited by
- 3,260
- References
- 85
- Citations by year
- 2026: 127 · 2025: 184 · 2024: 188 · 2023: 191 · 2022: 232 · 2021: 231 · 2020: 244 · 2019: 235 · 2018: 194 · 2017: 216 · 2016: 203 · 2015: 220 · 2014: 166 · 2013: 168 · 2012: 134
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- James S. McDonnell Foundation
- Deutsche Forschungsgemeinschaft
- National Institutes of Health
- National Institute on Drug Abuse (1R21 DA 15647-01, R21DA015647)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- human connectome, structural connectivity, structural brain networks, research strategies
- MeSH terms
- Animals, Brain, Humans, Nerve Net, Neurons, Synapses
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