Conserved and variable architecture of human white matter connectivity

Bassett, Danielle S.; Brown, Jesse A.; Deshpande, Vibhas; Carlson, Jean M.; Grafton, Scott T.

doi:10.1016/j.neuroimage.2010.09.006

Cited by 355 publications

(453 citation statements)

References 124 publications

(176 reference statements)

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“…Over the last years it has become clear that MR based connectomic techniques are of the highest interest for the neuroscience community (Bassett et al, 2010;Bullmore and Sporns, 2009;Fornito et al, 2010;Gross, 2008;Hagmann et al, 2010;Zalesky et al, 2010), but methodological issues remains. The presented method is a contribution to tackle these issues.…”

Section: Discussionmentioning

confidence: 99%

“…Given the increasing interest in such approaches as well as emerging questions about the optimal scale and optimal representation of such connection matrices (Bassett et al, 2010;Fornito et al, 2010;Zalesky et al, 2010), we take the opportunity to present in detail our approach to map the connectome at multiple scales and extensively test its reliability and reproducibility. Fig.…”

Section: Introductionmentioning

confidence: 99%

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Mapping the human connectome at multiple scales with diffusion spectrum MRI

Cammoun

Gigandet

Meskaldji

et al. 2012

Journal of Neuroscience Methods

459

525

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a b s t r a c tThe global structural connectivity of the brain, the human connectome, is now accessible at millimeter scale with the use of MRI. In this paper, we describe an approach to map the connectome by constructing normalized whole-brain structural connection matrices derived from diffusion MRI tractography at 5 different scales. Using a template-based approach to match cortical landmarks of different subjects, we propose a robust method that allows (a) the selection of identical cortical regions of interest of desired size and location in different subjects with identification of the associated fiber tracts (b) straightforward construction and interpretation of anatomically organized whole-brain connection matrices and (c) statistical inter-subject comparison of brain connectivity at various scales. The fully automated postprocessing steps necessary to build such matrices are detailed in this paper. Extensive validation tests are performed to assess the reproducibility of the method in a group of 5 healthy subjects and its reliability is as well considerably discussed in a group of 20 healthy subjects.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping the human connectome at multiple scales with diffusion spectrum MRI

Cammoun

Gigandet

Meskaldji

et al. 2012

Journal of Neuroscience Methods

459

525

View full text Add to dashboard Cite

show abstract

“…Even along the same WMM path, different positions of initiating ROIs may affect the network construction [138] . Bassett et al disclosed conserved and variable architectures of anatomical networks between the DTI and DSI techniques [139] . imaging, such as structural and functional MRI [4,140] , is useful for image alignment thanks to homogeneous registration.…”

Section: Future Directions Of Dmri For Brainnetomementioning

confidence: 99%

“…Many researchers have tried to investigate the reproducibility and reliability of the acquired networks through different imaging protocols [139,149,150] , different imaging sites [151,152] , and different parameters for constructing networks [19,153] . However, this is still an open question for the Brainnetome era [12] .…”

Section: Future Directions Of Dmri For Brainnetomementioning

confidence: 99%

Diffusion magnetic resonance imaging for Brainnetome: A critical review

Zuo¹,

Cheng²,

Jiang³

2012

Neurosci. Bull.

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Abstract:Increasing evidence shows that the human brain is a highly self-organized system that shows attributes of smallworldness, hierarchy and modularity. The "connectome" was conceived several years ago to identify the underpinning physical connectivities of brain networks. The need for an integration of multi-spatial and -temporal approaches is becoming apparent. Therefore, the "Brainnetome" (brain-net-ome) project was proposed. Diffusion magnetic resonance imaging (dMRI) is a non-invasive way to study the anatomy of brain networks. Here, we review the principles of dMRI, its methodologies, and some of its clinical applications for the Brainnetome. Future research in this field is discussed.

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“…This implies that, as more samples are drawn, more brain regions are likely to eventually become connected given a threshold at zero. Alternatively, the number of streamlines can be interpreted as connection weight (Bassett et al, 2011;Zalesky et al, 2010;Robinson et al, 2010), or a relative threshold can be applied (Kaden et al, 2007). This way, the relative differences between connections remain respected.…”

Section: Introductionmentioning

confidence: 99%

Bayesian inference of structural brain networks

et al. 2013

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Structural brain networks are used to model white-matter connectivity between spatially segregated brain regions. The presence, location and orientation of these white matter tracts can be derived using diffusion-weighted magnetic resonance imaging in combination with probabilistic tractography. Unfortunately, as of yet, none of the existing approaches provide an undisputed way of inferring brain networks from the streamline distributions which tractography produces. Stateof-the-art methods rely on an arbitrary threshold or, alternatively, yield weighted results that are difficult to interpret. In this paper, we provide a generative model that explicitly describes how structural brain networks lead to observed streamline distributions. This allows us to draw principled conclusions about brain networks, which we validate using simultaneously acquired resting-state functional MRI data. Inference may be further informed by means of a prior which combines connectivity estimates from multiple subjects. Based on this prior, we obtain networks that significantly improve on the conventional approach.

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Conserved and variable architecture of human white matter connectivity

Cited by 355 publications

References 124 publications

Mapping the human connectome at multiple scales with diffusion spectrum MRI

Mapping the human connectome at multiple scales with diffusion spectrum MRI

Diffusion magnetic resonance imaging for Brainnetome: A critical review

Bayesian inference of structural brain networks

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