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

Myelin plays a crucial role in how well information travels between brain regions. Many neurological diseases affect the myelin in the white matter, making myelin-sensitive metrics derived from quantitative MRI of potential interest for early detection and prognosis of those conditions. 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 how the myeloarchitecture relates to brain function. In this work we weight the connectome by the longitudinal relaxation rate (R1) as 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 is able to separate different classes (unimodal and transmodal), following a functional gradient. Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture. Author summary In the present work, we integrate a myelin sensitive MRI metric into the connectome and compare it with a connectome weighted with a standard diffusion-derived metric, number of streamlines (NOS). Our analysis shows that the R1-weighted connectome complements the NOS-weighted connectome. We show that the R1-weighted average distribution does not follow the same trend as the NOS strength distribution, and the two connectomes exhibit different modular organization. We also show that unimodal cortical regions tend to be connected by more streamlines, but the connections exhibit a lower R1-weighted average, while the transmodal regions tend to have a higher R1-weighted average but fewer streamlines. In terms of network communication, this could imply that the unimodal regions require more connections with lower myelination, whereas the transmodal regions take more myelinated, but fewer, connections for a reliable transfer of information.

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

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

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

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

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

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

[7]  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.

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

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

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

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

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

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

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

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

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

[17]  Stephen M. Smith,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

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

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

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

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

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

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

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

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

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

[27]  Ninon Burgos,et al.  New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .

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

[29]  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.

[30]  Weiliang Qiu,et al.  Novel Data Transformations for RNA-seq Differential Expression Analysis , 2019, Scientific Reports.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[45]  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.

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

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

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

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

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

[51]  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.

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

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

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

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

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

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

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

[59]  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.

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

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

[62]  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.

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

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

[65]  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.

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