The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure

Abstract Myelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture.

[1]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[2]  Susumu Mori,et al.  Fiber tracking: principles and strategies – a technical review , 2002, NMR in biomedicine.

[3]  Stefan Skare,et al.  How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging , 2003, NeuroImage.

[4]  A. Vespignani,et al.  The architecture of complex weighted networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[6]  P. Thiran,et al.  Mapping Human Whole-Brain Structural Networks with Diffusion MRI , 2007, PloS one.

[7]  Cornelis J. Stam,et al.  Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain , 2008, NeuroImage.

[8]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[9]  David H. Miller,et al.  Quantitative magnetic resonance of postmortem multiple sclerosis brain before and after fixation , 2008, Magnetic resonance in medicine.

[10]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[11]  R. Kahn,et al.  Aberrant Frontal and Temporal Complex Network Structure in Schizophrenia: A Graph Theoretical Analysis , 2010, The Journal of Neuroscience.

[12]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[13]  Olaf Sporns,et al.  Can structure predict function in the human brain? , 2010, NeuroImage.

[14]  Tobias Kober,et al.  MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field , 2010, NeuroImage.

[15]  Derek K. Jones.  Challenges and limitations of quantifying brain connectivity in vivo with diffusion MRI , 2010 .

[16]  Olaf Sporns,et al.  Weight-conserving characterization of complex functional brain networks , 2011, NeuroImage.

[17]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[18]  D. V. van Essen,et al.  Mapping Human Cortical Areas In Vivo Based on Myelin Content as Revealed by T1- and T2-Weighted MRI , 2011, The Journal of Neuroscience.

[19]  Mason A. Porter,et al.  Comparing Community Structure to Characteristics in Online Collegiate Social Networks , 2008, SIAM Rev..

[20]  M. P. van den Heuvel,et al.  Impaired Structural Motor Connectome in Amyotrophic Lateral Sclerosis , 2011, PloS one.

[21]  A. MacKay,et al.  Magnetic resonance imaging of myelin , 2007, Neurotherapeutics.

[22]  Mark W. Woolrich,et al.  FSL , 2012, NeuroImage.

[23]  Alan Connelly,et al.  Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information , 2012, NeuroImage.

[24]  Patric Hagmann,et al.  Mapping the human connectome at multiple scales with diffusion spectrum MRI , 2012, Journal of Neuroscience Methods.

[25]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[26]  Jeff H. Duyn,et al.  The contribution of myelin to magnetic susceptibility-weighted contrasts in high-field MRI of the brain , 2012, NeuroImage.

[27]  Alan Connelly,et al.  MRtrix: Diffusion tractography in crossing fiber regions , 2012, Int. J. Imaging Syst. Technol..

[28]  Stuart Crozier,et al.  Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images , 2012, NeuroImage.

[29]  O. Sporns,et al.  Network hubs in the human brain , 2013, Trends in Cognitive Sciences.

[30]  Thomas R. Knösche,et al.  White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI , 2013, NeuroImage.

[31]  Martijn P. van den Heuvel,et al.  Estimating false positives and negatives in brain networks , 2013, NeuroImage.

[32]  Richard F. Betzel,et al.  Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivity , 2013, Network Science.

[33]  Richard F. Betzel,et al.  Resting-brain functional connectivity predicted by analytic measures of network communication , 2013, Proceedings of the National Academy of Sciences.

[34]  M. P. van den Heuvel,et al.  Estimating false positives and negatives in brain networks. , 2013, NeuroImage.

[35]  Jan Sijbers,et al.  Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data , 2014, NeuroImage.

[36]  Derek K. Jones,et al.  Why diffusion tensor MRI does well only some of the time: Variance and covariance of white matter tissue microstructure attributes in the living human brain☆ , 2014, NeuroImage.

[37]  B. Wandell,et al.  Lifespan maturation and degeneration of human brain white matter , 2014, Nature Communications.

[38]  O. Sporns,et al.  Structural and Functional Aspects Relating to Cost and Benefit of Rich Club Organization in the Human Cerebral Cortex , 2013, Cerebral cortex.

[39]  Robert Turner,et al.  Myelin and iron concentration in the human brain: A quantitative study of MRI contrast , 2014, NeuroImage.

[40]  Tobias Kober,et al.  Robust T1-Weighted Structural Brain Imaging and Morphometry at 7T Using MP2RAGE , 2014, PloS one.

[41]  Nikolaus Weiskopf,et al.  Using high-resolution quantitative mapping of R1 as an index of cortical myelination , 2014, NeuroImage.

[42]  Julien Cohen-Adad,et al.  Quantitative magnetization transfer imaging made easy with qMTLab: Software for data simulation, analysis, and visualization , 2015 .

[43]  J. Rilling,et al.  Comparison of diffusion tractography and tract‐tracing measures of connectivity strength in rhesus macaque connectome , 2015, Human brain mapping.

[44]  Alan Connelly,et al.  SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography , 2015, NeuroImage.

[45]  Bibek Dhital,et al.  Gibbs‐ringing artifact removal based on local subvoxel‐shifts , 2015, Magnetic resonance in medicine.

[46]  Edward T. Bullmore,et al.  Fundamentals of Brain Network Analysis , 2016 .

[47]  J. Gore,et al.  The microstructural correlates of T1 in white matter , 2016, Magnetic resonance in medicine.

[48]  Jan Sijbers,et al.  Denoising of diffusion MRI using random matrix theory , 2016, NeuroImage.

[49]  Ben Jeurissen,et al.  T1 relaxometry of crossing fibres in the human brain , 2016, NeuroImage.

[50]  R. Ophoff,et al.  Brain network analysis reveals affected connectome structure in bipolar I disorder , 2016, Human brain mapping.

[51]  Jelle Veraart,et al.  Diffusion MRI noise mapping using random matrix theory , 2016, Magnetic resonance in medicine.

[52]  Derek K. Jones,et al.  Dynamics of the Human Structural Connectome Underlying Working Memory Training , 2016, The Journal of Neuroscience.

[53]  Richard F. Betzel,et al.  Modular Brain Networks. , 2016, Annual review of psychology.

[54]  Stamatios N. Sotiropoulos,et al.  An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging , 2016, NeuroImage.

[55]  Elizabeth Jefferies,et al.  Situating the default-mode network along a principal gradient of macroscale cortical organization , 2016, Proceedings of the National Academy of Sciences.

[56]  Peter F. Neher,et al.  The challenge of mapping the human connectome based on diffusion tractography , 2017, Nature Communications.

[57]  O. Sporns,et al.  Network neuroscience , 2017, Nature Neuroscience.

[58]  H. Johansen-Berg,et al.  White Matter Plasticity in the Adult Brain , 2017, Neuron.

[59]  Matteo Mancini,et al.  Introducing axonal myelination in connectomics: A preliminary analysis of g-ratio distribution in healthy subjects , 2018, NeuroImage.

[60]  Martijn P. van den Heuvel,et al.  An MRI Von Economo – Koskinas atlas , 2016, NeuroImage.

[61]  Richard Bowtell,et al.  Microstructural imaging of the human brain with a ‘super-scanner’: 10 key advantages of ultra-strong gradients for diffusion MRI , 2018, NeuroImage.

[62]  Alexander Leemans,et al.  Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics , 2018, Brain Structure and Function.

[63]  H. Johansen-Berg,et al.  Advances in noninvasive myelin imaging , 2017, Developmental neurobiology.

[64]  A. Petiet,et al.  Ultrahigh field imaging of myelin disease models: Toward specific markers of myelin integrity? , 2019, The Journal of comparative neurology.

[65]  Alan Connelly,et al.  MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation , 2019, NeuroImage.

[66]  Derek K. Jones,et al.  Optimization of graph construction can significantly increase the power of structural brain network studies , 2019, NeuroImage.

[67]  S. Berman,et al.  Modeling conduction delays in the corpus callosum using MRI-measured g-ratio , 2019, NeuroImage.

[68]  Derek K. Jones,et al.  Estimating axon conduction velocity in vivo from microstructural MRI , 2019, NeuroImage.

[69]  S. Aoki,et al.  MR g-ratio-weighted connectome analysis in patients with multiple sclerosis , 2019, Scientific Reports.

[70]  Laura E. Suárez,et al.  Gradients of structure–function tethering across neocortex , 2019, Proceedings of the National Academy of Sciences.

[71]  Fernando Calamante,et al.  The Seven Deadly Sins of Measuring Brain Structural Connectivity Using Diffusion MRI Streamlines Fibre-Tracking , 2019, Diagnostics.

[72]  Andrew Zalesky,et al.  Building connectomes using diffusion MRI: why, how and but , 2017, NMR in biomedicine.

[73]  W. Cahn,et al.  Evolutionary modifications in human brain connectivity associated with schizophrenia , 2019, Brain : a journal of neurology.

[74]  Reinder Vos de Wael,et al.  Microstructure-Informed Connectomics: Enriching Large-Scale Descriptions of Healthy and Diseased Brains , 2019, Brain Connect..

[75]  Chun-Hung Yeh,et al.  MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation , 2019, NeuroImage.

[76]  Ben Jeurissen,et al.  Diffusion MRI fiber tractography of the brain , 2019, NMR in biomedicine.

[77]  Derek K. Jones,et al.  Optimization of graph construction can significantly increase the power of structural brain network studies , 2019 .

[78]  Boris C. Bernhardt,et al.  Gradients of structure–function tethering across neocortex , 2019, Proceedings of the National Academy of Sciences.

[79]  Kotagiri Ramamohanarao,et al.  Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography? , 2018, Magnetic resonance in medicine.

[80]  Tobias Kober,et al.  Probing myelin content of the human brain with MRI: A review , 2020, Magnetic resonance in medicine.

[81]  Julien Cohen-Adad,et al.  qMRLab: Quantitative MRI analysis, under one umbrella , 2020, J. Open Source Softw..

[82]  Richard F. Betzel,et al.  Linking Structure and Function in Macroscale Brain Networks , 2020, Trends in Cognitive Sciences.

[83]  J. Thiran,et al.  A new method for accurate in vivo mapping of human brain connections using microstructural and anatomical information. , 2020, Science advances.

[84]  Chun-Hung Yeh,et al.  Mapping Structural Connectivity Using Diffusion MRI: Challenges and Opportunities , 2020, Journal of magnetic resonance imaging : JMRI.

[85]  Derek K. Jones,et al.  Noninvasive quantification of axon radii using diffusion MRI , 2020, eLife.

[86]  Jean-Philippe Thiran,et al.  A new method for accurate in vivo mapping of human brain connections using microstructural and anatomical information , 2020, Science Advances.

[87]  Thomas E. Nichols,et al.  An interactive meta-analysis of MRI biomarkers of myelin , 2020, eLife.

[88]  Thomas E. Nichols,et al.  An interactive meta-analysis of MRI biomarkers of myelin , 2020, bioRxiv.