The Human Connectome: A Structural Description of the Human Brain

Sporns, Olaf; Tononi, Giulio; Kötter, Rolf

doi:10.1371/journal.pcbi.0010042

Cited by 2,940 publications

(2,321 citation statements)

References 79 publications

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“…Understanding the structural basis of functional connectivity patterns requires a comprehensive map of structural connection of the human brain (the human connectome (Hagmann, 2005;Sporns et al, 2005)). We have just seen that advances in diffusion imaging and tractography methods permit the non-invasive mapping of white matter cortico-cortical and cortico-subcortical projections at high spatial resolution.…”

Section: From Tracts To Network 231 Past and Current State Of Thementioning

confidence: 99%

“…The perspective of collecting large amounts of connectional data combined with the understanding that the fundamental properties of the brain result from large-scale network topology has led two researchers, at that time independent, to realize the prime importance of this emerging technology, and to conceptualize in 2005 the notion of "connectome" and its related science "connectomics" (Hagmann, 2005;Sporns et al, 2005). In complete analogy to the word "genome", this new "-ome" emphasizes to the notion that the brain is one large and unique structural entity: a network made of neural connections (edges) and neural units (nodes).…”

Section: Introductionmentioning

confidence: 99%

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MR connectomics: Principles and challenges

Hagmann

Cammoun

Gigandet

et al. 2010

Journal of Neuroscience Methods

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207

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a b s t r a c t MR connectomics is an emerging framework in neuro-science that combines diffusion MRI and whole brain tractography methodologies with the analytical tools of network science. In the present work we review the current methods enabling structural connectivity mapping with MRI and show how such data can be used to infer new information of both brain structure and function. We also list the technical challenges that should be addressed in the future to achieve high-resolution maps of structural connectivity. From the resulting tremendous amount of data that is going to be accumulated soon, we discuss what new challenges must be tackled in terms of methods for advanced network analysis and visualization, as well data organization and distribution. This new framework is well suited to investigate key questions on brain complexity and we try to foresee what fields will most benefit from these approaches.

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Section: From Tracts To Network 231 Past and Current State Of Thementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MR connectomics: Principles and challenges

Hagmann

Cammoun

Gigandet

et al. 2010

Journal of Neuroscience Methods

Self Cite

256

207

View full text Add to dashboard Cite

show abstract

“…For the reconstruction of complete cellular wiring diagrams ("connectomes" [8][9] ) assuring reconstruction reliability is even more difficult because the morphologies of all neurons, not only those of a small subset, have to be extracted. This may eventually be possible at light-microscopic resolution by staining all neurons with a sufficient number of distinguishable colors 7,9 but otherwise requires imaging at a resolution high enough to follow all neurites in densely packed neuropil (discussed in 10 ).…”

Section: Introductionmentioning

confidence: 99%

High-accuracy neurite reconstruction for high-throughput neuroanatomy

2011

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Neuroanatomic analysis depends on the reconstruction of complete cell shapes. Highthroughput reconstruction of neural circuits ("connectomics") using volume electron microscopy requires dense staining of all cells, where even experts make annotation errors.Currently, reconstruction rather than acquisition speed limits the determination of neural wiring diagrams. We present methods for the fast and reliable reconstruction of densely labeled datasets. Our approach, based on manually skeletonizing each neurite redundantly (multiple times) with a special visualization/annotation software tool (KNOSSOS), is ~50 times faster than volume labeling. Errors are detected and eliminated by a "redundantskeleton consensus procedure" (RESCOP), which uses a statistical model of how true neurite connectivity is transformed into annotation decisions. RESCOP also estimates the consensus skeletons' reliability. Focused re-annotation of difficult locations promises a rather steep increase of reliability as a function of the average skeleton redundancy and thus the nearly error-free analysis of large neuroanatomical datasets.

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“…2011); also known and for the first time described as the human connectome (Sporns et al. 2005). Studies using graph analysis have shown that the human brain demonstrates small‐world properties, meaning that the network has a highly clustered local connectivity (greater than in a random network) and that there is a shorter path length (in terms of shortest distance) between brain regions than would be expected in a regular network (Watts and Strogatz 1998; Achard et al.…”

Section: Introductionmentioning

confidence: 99%

Reduced specialized processing in psychotic disorder: a graph theoretical analysis of cerebral functional connectivity

et al. 2016

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BackgroundPrevious research has shown that the human brain can be represented as a complex functional network that is characterized by specific topological properties, such as clustering coefficient, characteristic path length, and global/local efficiency. Patients with psychotic disorder may have alterations in these properties with respect to controls, indicating altered efficiency of network organization. This study examined graph theoretical changes in relation to differential genetic risk for the disorder and aimed to identify clinical correlates.MethodsAnatomical and resting‐state MRI brain scans were obtained from 73 patients with psychotic disorder, 83 unaffected siblings, and 72 controls. Topological measures (i.e., clustering coefficient, characteristic path length, and small‐worldness) were used as dependent variables in a multilevel random regression analysis to investigate group differences. In addition, associations with (subclinical) psychotic/cognitive symptoms were examined.ResultsPatients had a significantly lower clustering coefficient compared to siblings and controls, with no difference between the latter groups. No group differences were observed for characteristic path length and small‐worldness. None of the topological properties were associated with (sub)clinical psychotic and cognitive symptoms.ConclusionsThe reduced ability for specialized processing (reflected by a lower clustering coefficient) within highly interconnected brain regions observed in the patient group may indicate state‐related network alterations. There was no evidence for an intermediate phenotype and no evidence for psychopathology‐related alterations.

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The Human Connectome: A Structural Description of the Human Brain

Cited by 2,940 publications

References 79 publications

MR connectomics: Principles and challenges

MR connectomics: Principles and challenges

High-accuracy neurite reconstruction for high-throughput neuroanatomy

Reduced specialized processing in psychotic disorder: a graph theoretical analysis of cerebral functional connectivity

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