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

We present a new software package with a 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.

[1]  Marc Joliot,et al.  A population-based atlas of the human pyramidal tract in 410 healthy participants , 2018, Brain Structure and Function.

[2]  Daniel S. Margulies,et al.  An Open Resource for Non-human Primate Imaging , 2018, Neuron.

[3]  Heidi Johansen-Berg,et al.  The role of diffusion MRI in neuroscience , 2017, bioRxiv.

[4]  Hubertus J. A. van Hedel,et al.  Comparison of DTI analysis methods for clinical research: influence of pre-processing and tract selection methods , 2018, European Radiology Experimental.

[5]  Michael I. Miller,et al.  Elucidation of White Matter Tracts of the Human Amygdala by Detailed Comparison between High-Resolution Postmortem Magnetic Resonance Imaging and Histology , 2017, Front. Neuroanat..

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

[7]  Suzanne N Haber,et al.  Frontal Cortical and Subcortical Projections Provide a Basis for Segmenting the Cingulum Bundle: Implications for Neuroimaging and Psychiatric Disorders , 2014, The Journal of Neuroscience.

[8]  Stephen M. Smith,et al.  Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes , 2018, NeuroImage.

[9]  Paul M. Thompson,et al.  Investigating brain connectivity heritability in a twin study using diffusion imaging data , 2014, NeuroImage.

[10]  Tonya White,et al.  What Twin Studies Tell Us About the Heritability of Brain Development, Morphology, and Function: A Review , 2015, Neuropsychology Review.

[11]  R. Passingham,et al.  Whole brain comparative anatomy using connectivity blueprints , 2018, bioRxiv.

[12]  Silvio Sarubbo,et al.  Diffusion-based tractography atlas of the human acoustic radiation , 2019, Scientific Reports.

[13]  Andrew Simmons,et al.  Frontoparietal Tracts Linked to Lateralized Hand Preference and Manual Specialization , 2018, Cerebral cortex.

[14]  Derek K. Jones.  Studying connections in the living human brain with diffusion MRI , 2008, Cortex.

[15]  Emmanuel Procyk,et al.  Variations of cingulate sulcal organization and link with cognitive performance , 2018, Scientific Reports.

[16]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[17]  J. Kaufmann,et al.  Anatomically constrained tractography facilitates biologically plausible fiber reconstruction of the optic radiation in multiple sclerosis , 2019, NeuroImage: Clinical.

[18]  Peter F. Neher,et al.  TractSeg - Fast and accurate white matter tract segmentation , 2018, NeuroImage.

[19]  Jesper Andersson,et al.  A multi-modal parcellation of human cerebral cortex , 2016, Nature.

[20]  Matthew P. G. Allin,et al.  Atlasing location, asymmetry and inter-subject variability of white matter tracts in the human brain with MR diffusion tractography , 2011, NeuroImage.

[21]  Timothy E. J. Behrens,et al.  Measuring macroscopic brain connections in vivo , 2015, Nature Neuroscience.

[22]  Franck Ramus,et al.  Altered hemispheric lateralization of white matter pathways in developmental dyslexia: Evidence from spherical deconvolution tractography , 2016, Cortex.

[23]  Erin E. Hecht,et al.  Virtual dissection and comparative connectivity of the superior longitudinal fasciculus in chimpanzees and humans , 2015, NeuroImage.

[24]  Hugues Duffau,et al.  Middle longitudinal fasciculus delineation within language pathways: a diffusion tensor imaging study in human. , 2013, European journal of radiology.

[25]  Carl-Fredrik Westin,et al.  Automatic Tractography Segmentation Using a High-Dimensional White Matter Atlas , 2007, IEEE Transactions on Medical Imaging.

[26]  Mark Jenkinson,et al.  Cross-species cortical alignment identifies different types of neuroanatomical reorganization in higher primates , 2019 .

[27]  Ludovica Griffanti,et al.  Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank , 2017, NeuroImage.

[28]  M. Catani,et al.  A diffusion tensor imaging tractography atlas for virtual in vivo dissections , 2008, Cortex.

[29]  M. E. Shenton,et al.  Human middle longitudinal fascicle: segregation and behavioral-clinical implications of two distinct fiber connections linking temporal pole and superior temporal gyrus with the angular gyrus or superior parietal lobule using multi-tensor tractography , 2013, Brain Imaging and Behavior.

[30]  Karla L. Miller,et al.  The extreme capsule fiber complex in humans and macaque monkeys: a comparative diffusion MRI tractography study , 2015, Brain Structure and Function.

[31]  Manuel Graña,et al.  Model‐based analysis of multishell diffusion MR data for tractography: How to get over fitting problems , 2012, Magnetic resonance in medicine.

[32]  Sebastien Ourselin,et al.  Meyer's loop asymmetry and language lateralisation in epilepsy , 2015, Journal of Neurology, Neurosurgery & Psychiatry.

[33]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[34]  Michel Thiebaut de Schotten,et al.  Short frontal lobe connections of the human brain , 2012, Cortex.

[35]  Mark W. Woolrich,et al.  Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.

[36]  Guy B. Williams,et al.  QuickBundles, a Method for Tractography Simplification , 2012, Front. Neurosci..

[37]  J. Dubois,et al.  Diffusion tensor imaging of brain development. , 2006, Seminars in fetal & neonatal medicine.

[38]  Marcel Kinsbourne,et al.  CHAPTER 24 – Lateralization of Language across the Life Span , 2008 .

[39]  Peter Vajkoczy,et al.  Manual for clinical language tractography , 2019, Acta Neurochirurgica.

[40]  Matthew F. Glasser,et al.  Towards HCP-Style macaque connectomes: 24-Channel 3T multi-array coil, MRI sequences and preprocessing , 2020, NeuroImage.

[41]  Michel Thiebaut de Schotten,et al.  A revised limbic system model for memory, emotion and behaviour , 2013, Neuroscience & Biobehavioral Reviews.

[42]  Stamatios N. Sotiropoulos,et al.  Towards HCP-Style macaque connectomes: 24-Channel 3T multi-array coil, MRI sequences and preprocessing , 2019, NeuroImage.

[43]  Rogier B. Mars,et al.  Large-scale comparative neuroimaging: Where are we and what do we need? , 2019, Cortex.

[44]  B. Mazoyer,et al.  Cortical Terminations of the Inferior Fronto-Occipital and Uncinate Fasciculi: Anatomical Stem-Based Virtual Dissection , 2016, Front. Neuroanat..

[45]  R. Luján.  Fiber Pathways of the Brain, J.D. Schmahmann, D.N. Pandya (Eds.). Oxford University Press (2006), ISBN: 0-19-510423-4 , 2008 .

[46]  M. Raichle,et al.  Tracking neuronal fiber pathways in the living human brain. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[47]  Xiaoying Wu,et al.  Automated diffusion tensor tractography: implementation and comparison to user-driven tractography. , 2012, Academic radiology.

[48]  M. Catani,et al.  Diffusion-based tractography in neurological disorders: concepts, applications, and future developments , 2008, The Lancet Neurology.

[49]  Saad Jbabdi,et al.  Connectivity Fingerprints: From Areal Descriptions to Abstract Spaces , 2018, Trends in Cognitive Sciences.

[50]  Bruce Fischl,et al.  AnatomiCuts: Hierarchical clustering of tractography streamlines based on anatomical similarity , 2016, NeuroImage.

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

[52]  C. Lebel,et al.  Diffusion tensor imaging of white matter tract evolution over the lifespan , 2012, NeuroImage.

[53]  Mark Jenkinson,et al.  Cross-species cortical alignment identifies different types of neuroanatomical reorganization in the temporal lobe of higher primates , 2019, bioRxiv.

[54]  Deepak N. Pandya,et al.  The prefrontal cortex: Comparative architectonic organization in the human and the macaque monkey brains , 2012, Cortex.

[55]  R. Kahn,et al.  Heritability of structural brain network topology: A DTI study of 156 twins , 2014, Human brain mapping.

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

[57]  D. Pandya,et al.  Delineation of the middle longitudinal fascicle in humans: a quantitative, in vivo, DT-MRI study. , 2009, Cerebral cortex.

[58]  M. Jenkinson,et al.  Non-linear optimisation FMRIB Technial Report TR 07 JA 1 , 2007 .

[59]  Carl-Fredrik Westin,et al.  The Fiber Laterality Histogram: A New Way to Measure White Matter Asymmetry , 2010, MICCAI.

[60]  Randy L. Gollub,et al.  Reproducibility of quantitative tractography methods applied to cerebral white matter , 2007, NeuroImage.

[61]  Derek B Archer,et al.  A Template and Probabilistic Atlas of the Human Sensorimotor Tracts using Diffusion MRI , 2018, Cerebral cortex.

[62]  Derek K. Jones,et al.  Virtual in Vivo Interactive Dissection of White Matter Fasciculi in the Human Brain , 2002, NeuroImage.

[63]  Khader M. Hasan,et al.  Mapping the trajectory of the stria terminalis of the human limbic system using high spatial resolution diffusion tensor tractography , 2015, Neuroscience Letters.

[64]  S. Wakana,et al.  Fiber tract-based atlas of human white matter anatomy. , 2004, Radiology.

[65]  R. Pașcalău,et al.  Anatomy of the Limbic White Matter Tracts as Revealed by Fiber Dissection and Tractography. , 2018, World neurosurgery.

[66]  Christopher Rorden,et al.  Image Processing and Quality Control for the first 10,000 Brain Imaging Datasets from UK Biobank , 2017 .

[67]  Susumu Mori,et al.  Automated fiber tracking of human brain white matter using diffusion tensor imaging , 2008, NeuroImage.

[68]  M. Catani,et al.  A lateralized brain network for visuospatial attention , 2011, Nature Neuroscience.

[69]  Mark Jenkinson,et al.  Cross-species cortical alignment identifies different types of anatomical reorganization in the primate temporal lobe , 2020, eLife.

[70]  P. Matthews,et al.  Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.

[71]  Mats Fredrikson,et al.  Segmentation of the inferior longitudinal fasciculus in the human brain: A white matter dissection and diffusion tensor tractography study , 2017, Brain Research.

[72]  Steen Moeller,et al.  Advances in diffusion MRI acquisition and processing in the Human Connectome Project , 2013, NeuroImage.

[73]  A. Dale,et al.  Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry. , 2010, Cerebral cortex.

[74]  Jean-Philippe Thiran,et al.  Structural connectomics in brain diseases , 2013, NeuroImage.

[75]  D. Mantini,et al.  What is special about the human arcuate fasciculus? Lateralization, projections, and expansion , 2019, Cortex.

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

[77]  Klaas E. Stephan,et al.  The anatomical basis of functional localization in the cortex , 2002, Nature Reviews Neuroscience.

[78]  L. Zollei,et al.  A combined fMRI and DTI examination of functional language lateralization and arcuate fasciculus structure: Effects of degree versus direction of hand preference , 2010, Brain and Cognition.

[79]  Fang-Cheng Yeh,et al.  A Quantitative Tractography Study Into the Connectivity, Segmentation and Laterality of the Human Inferior Longitudinal Fasciculus , 2018, bioRxiv.

[80]  Peter A. Calabresi,et al.  Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification , 2008, NeuroImage.

[81]  D. V. van Essen,et al.  Windows on the brain: the emerging role of atlases and databases in neuroscience , 2002, Current Opinion in Neurobiology.

[82]  Franco Pestilli,et al.  Occipital white matter tracts in human and macaque , 2016, bioRxiv.

[83]  S. Mori,et al.  In vivo magnetic resonance imaging of the human limbic white matter , 2014, Front. Aging Neurosci..

[84]  Stefan Klein,et al.  Improving alignment in Tract-based spatial statistics: Evaluation and optimization of image registration , 2013, NeuroImage.

[85]  Carl-Fredrik Westin,et al.  The white matter query language: a novel approach for describing human white matter anatomy , 2015, Brain Structure and Function.

[86]  P. Roy,et al.  Age-Related Differences in White Matter Integrity in Healthy Human Brain: Evidence from Structural MRI and Diffusion Tensor Imaging , 2016, Magnetic resonance insights.

[87]  Alistair A. Young,et al.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2017, MICCAI 2017.