Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review

Zhang, Fan; Daducci, Alessandro; He, Yong; Schiavi, Simona; Seguin, Caio; Smith, Robert E.; Yeh, Chun‐Hung; Zhao, Tengda; O’Donnell, Lauren J.

doi:10.1016/j.neuroimage.2021.118870

Cited by 110 publications

(50 citation statements)

References 550 publications

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“…There are currently two principal methods available for imaging the SCs of the human brain: white matter tractography-based methods using diffusion weighted imaging and gray matter morphometry-based methods using structural imaging. Specifically, tractography-based connectomes can be reconstructed by inferring axonal tracts among brain regions using deterministic or probabilistic tractography approaches (Gong et al, 2009a; Hagmann et al, 2008; Zhang et al, 2021), while morphometry-based connectomes can be obtained by examining the statistical similarity of morphometric measures among regions (He et al, 2007; Kong et al, 2015; Seidlitz et al, 2018; Tijms et al, 2012). Although there are mounting reports showing FC-SC coupling (Baum et al, 2019; Honey et al, 2009; Misic et al, 2016; Wang et al, 2015b), whether and how interindividual FC variability patterns are structurally constrained is understudied.…”

Section: Introductionmentioning

confidence: 99%

Structural insight into the individual variability architecture of the functional brain connectome

Sun

Zhao

Liang

et al. 2022

Preprint

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Human cognition and behaviors depend upon the brain's functional connectomes, which vary remarkably across individuals. However, whether and how the functional connectome individual variability architecture is structurally constrained remains largely unknown. Using tractography- and morphometry-based network models, we observed the spatial convergence of structural and functional connectome individual variability, with higher variability in heteromodal association regions and lower variability in primary regions. We demonstrated that functional variability is significantly predicted by a unifying structural variability pattern and that this prediction follows a primary-to-heteromodal hierarchical axis, with higher prediction accuracy in primary regions and lower accuracy in heteromodal regions. We also decomposed group-level connectome variability patterns into individual unique contributions and uncovered the structural-functional correspondence that is associated with individual cognitive traits. These results advance our understanding of the structural basis of individual functional variability and suggest the importance of integrating multimodal connectome signatures for individual differences in cognition and behaviors.

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Section: Introductionmentioning

confidence: 99%

Structural insight into the individual variability architecture of the functional brain connectome

Sun

Zhao

Liang

et al. 2022

Preprint

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“…In the absence of reliable cross-hemispheric SC, restricting our analyses to single hemispheres mitigated the possibility of confounding intra-and inter-hemispheric partition similarity. More broadly, variations in SC mapping methods can impact the computation of network communication models [82][83][84] and future work is necessary to replicate our findings in alternative reconstructions of structural brain networks. We adopted the 7 resting-state networks defined by Yeo and colleagues as the reference partition for the mesoscale functional organization of the human brain [22].…”

Section: Limitations and Future Directionsmentioning

confidence: 90%

Network communication models narrow the gap between the modular organization of structural and functional brain networks

Seguin

Sporns

et al. 2022

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Structural and functional brain networks are modular. Canonical functional systems, such as the default mode network, are well-known modules of the human brain and have been implicated in a large number of cognitive, behavioral and clinical processes. However, modules delineated in structural brain networks inferred from tractography generally do not recapitulate canonical functional systems. Neuroimaging evidence suggests that functional connectivity between regions in the same systems is not always underpinned by anatomical connections. As such, direct structural connectivity alone would be insufficient to characterize the functional modular organization of the brain. Here, we demonstrate that augmenting structural brain networks with models of indirect (polysynaptic) communication unveils a modular network architecture that more closely resembles the brain's established functional systems. We find that diffusion models of polysynaptic connectivity, particularly communicability, narrow the gap between the modular organization of structural and functional brain networks by 20-60%, whereas routing models based on single efficient paths do not improve mesoscopic structure-function correspondence. This suggests that functional modules emerge from the constraints imposed by local network structure that facilitates diffusive neural communication. Our work establishes the importance of modeling polysynaptic communication to understand the structural basis of functional systems.

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“…Diffusion magnetic resonance imaging (dMRI) is the primary noninvasive in vivo technique for investigations of white matter fiber properties and the structural network of the human brain, such as the voxel-wise tract-based spatial statistics using diffusion tensor metrics (e.g. fractional anisotropy and mean diffusivity) (Smith et al, 2006), or measuring the topological properties of the complex human brain organization using tractography-based analysis (Bastiani and Roebroeck, 2015; Yeh et al, 2021; Zhang et al, 2021). Since the last decade, dMRI technologies have been rapidly advanced in both hardware and software.…”

Section: Introductionmentioning

confidence: 99%

Integrated Diffusion Image Operator (iDIO): A tool for automated configuration and processing of diffusion MRI data

Hsu

Chong

Kung

et al. 2022

Preprint

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The preprocessing of diffusion magnetic resonance imaging (dMRI) data involves numerous steps, including the corrections for head motion, susceptibility distortion, low signal-to-noise ratio, and signal drifting. Researchers or clinical practitioners often need to configure different preprocessing steps depending on disparate image acquisition schemes, which increases the technical threshold for dMRI analysis for non-expert users. This could cause disparities in data processing approaches and thus hinder the comparability between studies. To make the dMRI data processing steps transparent and adapt to various dMRI acquisition schemes for researchers, we propose a semi-automated pipeline tool for dMRI named integrated Diffusion Image Operator or iDIO. This pipeline integrates features from a wide range of advanced dMRI software tools and targets at providing a one-click solution for dMRI data analysis, via automatic configuration for a set of optimal processing steps based on the image header of the input data. Additionally, the pipeline provides options for post-processing, such as estimation of diffusion tensor metrics and whole-brain tractography-based connectomes reconstruction using common brain atlases. The iDIO pipeline also outputs an easy-to-interpret quality control report to facilitate users to assess the data quality. To keep the transparency of data processing, the execution log and all the intermediate images produced in the iDIO's workflow are accessible. The goal of iDIO is to reduce the barriers for clinical or non-specialist users to adopt the state-of-art dMRI processing steps.

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Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review

Cited by 110 publications

References 550 publications

Structural insight into the individual variability architecture of the functional brain connectome

Structural insight into the individual variability architecture of the functional brain connectome

Network communication models narrow the gap between the modular organization of structural and functional brain networks

Integrated Diffusion Image Operator (iDIO): A tool for automated configuration and processing of diffusion MRI data

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