Whole brain comparative anatomy using connectivity blueprints

Mars, Rogier B.; Sotiropoulos, Stamatios N.; Passingham, Richard E.; Sallet, Jérôme; Verhagen, Lennart; Khrapitchev, Alexandre A.; Sibson, Nicola R.; Jbabdi, Saâd

doi:10.7554/elife.35237

Cited by 150 publications

(169 citation statements)

References 45 publications

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“…This "fingerprint" problem has recently been explored in neuroimaging (Finn et al, 2015;Liu, Liao, Xia, & He, 2018;Mars et al, 2018). This "fingerprint" problem has recently been explored in neuroimaging (Finn et al, 2015;Liu, Liao, Xia, & He, 2018;Mars et al, 2018).…”

Section: Test-retest Reproducibilitymentioning

confidence: 99%

Test–retest reproducibility of a multi‐atlas automated segmentation tool on multimodality brain MRI

et al. 2019

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Introduction The increasing use of large sample sizes for population and personalized medicine requires high‐throughput tools for imaging processing that can handle large amounts of data with diverse image modalities, perform a biologically meaningful information reduction, and result in comprehensive quantification. Exploring the reproducibility of these tools reveals the specific strengths and weaknesses that heavily influence the interpretation of results, contributing to transparence in science. Methods We tested–retested the reproducibility of MRICloud, a free automated method for whole‐brain, multimodal MRI segmentation and quantification, on two public, independent datasets of healthy adults. Results The reproducibility was extremely high for T1‐volumetric analysis, high for diffusion tensor images (DTI) (however, regionally variable), and low for resting‐state fMRI. Conclusion In general, the reproducibility of the different modalities was slightly superior to that of widely used software. This analysis serves as a normative reference for planning samples and for the interpretation of structure‐based MRI studies.

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Section: Test-retest Reproducibilitymentioning

confidence: 99%

Test–retest reproducibility of a multi‐atlas automated segmentation tool on multimodality brain MRI

et al. 2019

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“…To ensure generalisability of the protocols across species, we also utilised diffusion MRI data from the macaque brain. This data consisted of an extended set of animals used in (Eichert et al, 2019a;Mars et al, 2018b). In total, the datasets we considered consist of 1065 subjects from the HCP (all available HCP S1200 subjects that had diffusion MRI data), 1000 subjects from the UK Biobank and ex vivo high-resolution datasets from 6 macaques.…”

Section: Data and Preprocessingmentioning

confidence: 99%

“…For the macaque data, we combined data previously used in (Mars et al, 2018b) with newly acquired data. Data were acquired locally on a 7T magnet with an Agilent DirectDrive console (Agilent Technologies, Santa Clara, CA, USA) using a 2D diffusion-weighted spin-echo protocol with single line readout (DW-SEMS, TE/TR: 25 ms/10 s; matrix size: 128 x 128; resolution: 0.6 x 0.6 mm; number of slices: 128; slice thickness: 0.6 mm; diffusion data were acquired over the course of 53 hours).…”

Section: Data and Preprocessingmentioning

confidence: 99%

“…The virtual dissection of major WM tracts enables the study of brain organisation (Catani et al, 2013;Jbabdi et al, 2015) and offers a probe to brain development (Huppi and Dubois, 2006) and WM pathology (Ciccarelli et al, 2008;Griffa et al, 2013). It further allows explorations of individual variations (Assaf et al, 2017) and crossspecies variations (Mars et al, 2018b) in anatomy and connectivity. This information has functional relevance, as the pattern of extrinsic WM connections of each functional brain subunit to the rest of the brain are unique (Mars et al, 2018a;Passingham et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…More recently the power of automated tractography has been illustrated within a different context. (Mars et al, 2018b) defined a set of tracts, derived from the same tractography protocols as in (de Groot et al, 2013), in both humans and macaques, in order to perform comparative anatomy. They defined connectivity blueprints as (Cortex) x (Tracts) maps, which can be used to identify functionally equivalent cortical regions between the two different species, in the absence of any geometrical similarity.…”

Section: Introductionmentioning

confidence: 99%

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XTRACT - Standardised protocols for automated tractography in the human and macaque brain

Warrington

Bryant

et al. 2019

Preprint

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We present a new toolbox and library of standardised tractography protocols devised for the robust automated extraction of white matter tracts both in the human and the macaque brain. Using in vivo data from the Human Connectome Project (HCP) and the UK Biobank and ex vivo data for the macaque brain datasets, we obtain white matter atlases, as well as atlases for tract endpoints on the white-grey matter boundary, for both species. We illustrate that our protocols are robust against data quality, generalisable across two species and reflect the known anatomy. We further demonstrate that they capture inter-subject variability by preserving tract lateralisation in humans and tract similarities stemming from twinship in the HCP cohort. Our results demonstrate that the presented toolbox will be useful for generating imaging-derived features in large cohorts, and in facilitating comparative neuroanatomy studies. The software, tractography protocols, and atlases are publicly released through FSL, allowing users to define their own tractography protocols in a standardised manner, further contributing to open science.

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Connectivity gradients on tractography data: Pipeline and example applications

Freches

Haak

Beckmann

et al. 2021

Human Brain Mapping

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Gray matter connectivity can be described in terms of its topographical organization, but the differential role of white matter connections underlying that organization is often unknown. In this study, we propose a method for unveiling principles of organization of both gray and white matter based on white matter connectivity as assessed using diffusion magnetic ressonance imaging (MRI) tractography with spectral embedding gradient mapping. A key feature of the proposed approach is its capacity to project the individual connectivity gradients it reveals back onto its input data in the form of projection images, allowing one to assess the contributions of specific white matter tracts to the observed gradients. We demonstrate the ability of our proposed pipeline to identify connectivity gradients in prefrontal and occipital gray matter. Finally, leveraging the use of tractography, we demonstrate that it is possible to observe gradients within the white matter bundles themselves. Together, the proposed framework presents a generalized way to assess both the topographical organization of structural brain connectivity and the anatomical features driving it.

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Whole brain comparative anatomy using connectivity blueprints

Cited by 150 publications

References 45 publications

Test–retest reproducibility of a multi‐atlas automated segmentation tool on multimodality brain MRI

Test–retest reproducibility of a multi‐atlas automated segmentation tool on multimodality brain MRI

XTRACT - Standardised protocols for automated tractography in the human and macaque brain

Connectivity gradients on tractography data: Pipeline and example applications

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