Probabilistic atlases of default mode, executive control and salience network white matter tracts: an fMRI-guided diffusion tensor imaging and tractography study

Diffusion tensor imaging (DTI) is a powerful MRI technique that can be used to estimate both the microstructural integrity and the trajectories of white matter pathways throughout the central nervous system. This fiber tracking (aka, “tractography”) approach is often carried out using anatomically-defined seed points to identify white matter tracts that pass through one or more structures, but can also be performed using functionally-defined regions of interest (ROIs) that have been determined using functional MRI (fMRI) or other methods. In this study, we performed fMRI-guided DTI tractography between all of the previously defined nodes within each of six common resting-state brain networks, including the: dorsal Default Mode Network (dDMN), ventral Default Mode Network (vDMN), left Executive Control Network (lECN), right Executive Control Network (rECN), anterior Salience Network (aSN), and posterior Salience Network (pSN). By normalizing the data from 32 healthy control subjects to a standard template—using high-dimensional, non-linear warping methods—we were able to create probabilistic white matter atlases for each tract in stereotaxic coordinates. By investigating all 198 ROI-to-ROI combinations within the aforementioned resting-state networks (for a total of 6336 independent DTI tractography analyses), the resulting probabilistic atlases represent a comprehensive cohort of functionally-defined white matter regions that can be used in future brain imaging studies to: (1) ascribe DTI or other white matter changes to particular functional brain networks, and (2) compliment resting state fMRI or other functional connectivity analyses.

[1]  Alain Trouvé,et al.  Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms , 2005, International Journal of Computer Vision.

[2]  M. Solaiyappan,et al.  In vivo three‐dimensional reconstruction of rat brain axonal projections by diffusion tensor imaging , 1999, Magnetic resonance in medicine.

[3]  Jung E. Park,et al.  The Problem of Functional , 2015, Journal of movement disorders.

[4]  D. Tuch Q‐ball imaging , 2004, Magnetic resonance in medicine.

[5]  V. Menon Large-Scale Brain Networks in Cognition: Emerging Principles , 2010 .

[6]  P. Hagmann,et al.  Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[7]  Alan C. Evans,et al.  An MRI-based stereotactic atlas from 250 young normal subjects , 1992 .

[8]  M. Miller,et al.  Correction of B0 susceptibility induced distortion in diffusion-weighted images using large-deformation diffeomorphic metric mapping. , 2008, Magnetic resonance imaging.

[9]  Martijn P. van den Heuvel,et al.  Affected connectivity organization of the reward system structure in obesity , 2015, NeuroImage.

[10]  Yong He,et al.  Understanding Structural-Functional Relationships in the Human Brain , 2015, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[11]  Danielle Smith Bassett,et al.  Small-World Brain Networks , 2006, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[12]  V. Menon,et al.  Saliency, switching, attention and control: a network model of insula function , 2010, Brain Structure and Function.

[13]  Edward M Callaway,et al.  Cell type specificity of local cortical connections , 2002, Journal of neurocytology.

[14]  Derek K. Jones,et al.  Spatial Normalization and Averaging of Diffusion Tensor MRI Data Sets , 2002, NeuroImage.

[15]  Timothy Edward John Behrens,et al.  Triangulating a Cognitive Control Network Using Diffusion-Weighted Magnetic Resonance Imaging (MRI) and Functional MRI , 2007, The Journal of Neuroscience.

[16]  Thomas E. Nichols,et al.  Functional connectomics from resting-state fMRI , 2013, Trends in Cognitive Sciences.

[17]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[18]  S. Mori,et al.  Principles of Diffusion Tensor Imaging and Its Applications to Basic Neuroscience Research , 2006, Neuron.

[19]  S. Bressler,et al.  Large-scale brain networks in cognition: emerging methods and principles , 2010, Trends in Cognitive Sciences.

[20]  David C. Van Essen,et al.  The future of the human connectome , 2012, NeuroImage.

[21]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[22]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[23]  Jerry L. Prince,et al.  Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T , 2007, NeuroImage.

[24]  J. Shimony,et al.  Resting-State fMRI: A Review of Methods and Clinical Applications , 2013, American Journal of Neuroradiology.

[25]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[26]  I. Koerte,et al.  Diffusion Tensor Imaging , 2014 .

[27]  B. Wandell,et al.  Tract Profiles of White Matter Properties: Automating Fiber-Tract Quantification , 2012, PloS one.

[28]  Arthur W. Toga,et al.  Construction of a 3D probabilistic atlas of human cortical structures , 2008, NeuroImage.

[29]  M. Horsfield,et al.  Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging , 1999, Magnetic resonance in medicine.

[30]  P. V. van Zijl,et al.  Three‐dimensional tracking of axonal projections in the brain by magnetic resonance imaging , 1999, Annals of neurology.

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

[32]  Arthur W. Toga,et al.  Human brain white matter atlas: Identification and assignment of common anatomical structures in superficial white matter , 2008, NeuroImage.

[33]  Olaf Sporns,et al.  What Is the Human Connectome , 2009 .

[34]  Michael I. Miller,et al.  Multi-contrast large deformation diffeomorphic metric mapping for diffusion tensor imaging , 2009, NeuroImage.

[35]  Milan Sonka,et al.  3D Slicer as an image computing platform for the Quantitative Imaging Network. , 2012, Magnetic resonance imaging.

[36]  I. Johnsrude,et al.  The problem of functional localization in the human brain , 2002, Nature Reviews Neuroscience.

[37]  O. Sporns,et al.  Rich-Club Organization of the Human Connectome , 2011, The Journal of Neuroscience.

[38]  Rachid Deriche,et al.  Quantitative Comparison of Reconstruction Methods for Intra-Voxel Fiber Recovery From Diffusion MRI , 2014, IEEE Transactions on Medical Imaging.

[39]  Arthur W. Toga,et al.  Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template , 2008, NeuroImage.

[40]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[41]  O. Sporns,et al.  The economy of brain network organization , 2012, Nature Reviews Neuroscience.

[42]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

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

[44]  M. Greicius,et al.  Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity , 2009, Brain Structure and Function.

[45]  Abraham Z. Snyder,et al.  A default mode of brain function: A brief history of an evolving idea , 2007, NeuroImage.

[46]  Paul J. Laurienti,et al.  An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets , 2003, NeuroImage.

[47]  G. Glover,et al.  Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control , 2007, The Journal of Neuroscience.

[48]  Andreas Horn,et al.  The structural–functional connectome and the default mode network of the human brain , 2014, NeuroImage.

[49]  Hangyi Jiang,et al.  DtiStudio: Resource program for diffusion tensor computation and fiber bundle tracking , 2006, Comput. Methods Programs Biomed..

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

[51]  Arthur W. Toga,et al.  A Probabilistic Atlas of the Human Brain: Theory and Rationale for Its Development The International Consortium for Brain Mapping (ICBM) , 1995, NeuroImage.

[52]  Michael J. Martinez,et al.  Bias between MNI and Talairach coordinates analyzed using the ICBM‐152 brain template , 2007, Human brain mapping.

[53]  M. Greicius,et al.  Decoding subject-driven cognitive states with whole-brain connectivity patterns. , 2012, Cerebral cortex.

[54]  Feng Zhou,et al.  Object Working Memory Performance Depends on Microstructure of the Frontal-Occipital Fasciculus , 2011, Brain Connect..

[55]  M. Raichle,et al.  Searching for a baseline: Functional imaging and the resting human brain , 2001, Nature Reviews Neuroscience.

[56]  David G. Norris,et al.  An Investigation of Functional and Anatomical Connectivity Using Magnetic Resonance Imaging , 2002, NeuroImage.

[57]  O Sporns,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009, Proceedings of the National Academy of Sciences.

[58]  Arthur W. Toga,et al.  Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: Application to normal elderly and Alzheimer's disease participants , 2009, NeuroImage.

[59]  Mara Cercignani,et al.  Twenty‐five pitfalls in the analysis of diffusion MRI data , 2010, NMR in biomedicine.

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

[61]  James C. Gee,et al.  Spatial transformations of diffusion tensor magnetic resonance images , 2001, IEEE Transactions on Medical Imaging.

[62]  J L Lancaster,et al.  Automated Talairach Atlas labels for functional brain mapping , 2000, Human brain mapping.

[63]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[64]  Pablo Villoslada,et al.  Evaluating Structural Connectomics in Relation to Different Q-space Sampling Techniques , 2013, MICCAI.

[65]  Timothy E. Ham,et al.  Cognitive Control and the Salience Network: An Investigation of Error Processing and Effective Connectivity , 2013, The Journal of Neuroscience.

[66]  Ning Yang,et al.  Greater Than the Sum of Its Parts , 2010, IEEE Microwave Magazine.

[67]  Kimberly L. Ray,et al.  Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions , 2012, Cognitive, affective & behavioral neuroscience.

[68]  J. Tournier,et al.  High Angular Resolution Diffusion Imaging , 2016 .

[69]  D. Schacter,et al.  The Brain's Default Network , 2008, Annals of the New York Academy of Sciences.

[70]  S Skare,et al.  Condition number as a measure of noise performance of diffusion tensor data acquisition schemes with MRI. , 2000, Journal of magnetic resonance.

[71]  J. Haynes,et al.  Can we overcome the ‘clinico-radiological paradox’ in multiple sclerosis? , 2012, Journal of Neurology.

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

[73]  D. Shen,et al.  Spatial normalization of diffusion tensor fields , 2003, Magnetic resonance in medicine.

[74]  Patrick J. F. Groenen,et al.  Modern Multidimensional Scaling: Theory and Applications , 2003 .

[75]  N. Makris,et al.  High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity , 2002, Magnetic resonance in medicine.

[76]  K. Amunts,et al.  Towards multimodal atlases of the human brain , 2006, Nature Reviews Neuroscience.

[77]  Jerry L Prince,et al.  Effects of signal‐to‐noise ratio on the accuracy and reproducibility of diffusion tensor imaging–derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T , 2007, Journal of magnetic resonance imaging : JMRI.

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

[79]  M. Corbetta,et al.  Increased functional connectivity indicates the severity of cognitive impairment in multiple sclerosis , 2011, Proceedings of the National Academy of Sciences.

[80]  Peter Fransson,et al.  The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis , 2008, NeuroImage.

[81]  F. Barkhof The clinico‐radiological paradox in multiple sclerosis revisited , 2002, Current opinion in neurology.

[82]  Paul M. Thompson,et al.  Along-tract statistics allow for enhanced tractography analysis , 2012, NeuroImage.

[83]  Brian A. Wandell,et al.  Anatomical Properties of the Arcuate Fasciculus Predict Phonological and Reading Skills in Children , 2011, Journal of Cognitive Neuroscience.

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

[85]  Heidi Johansen-Berg,et al.  Using diffusion imaging to study human connectional anatomy. , 2009, Annual review of neuroscience.

[86]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[87]  M. Torrens Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .

[88]  Arthur W. Toga,et al.  Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy , 2010, NeuroImage.

[89]  C. Beaulieu,et al.  The basis of anisotropic water diffusion in the nervous system – a technical review , 2002, NMR in biomedicine.

[90]  Lucina Q. Uddin,et al.  Complex relationships between structural and functional brain connectivity , 2013, Trends in Cognitive Sciences.

[91]  M. Greicius,et al.  Resting-state functional connectivity reflects structural connectivity in the default mode network. , 2009, Cerebral cortex.

[92]  Terry M. Peters,et al.  3D statistical neuroanatomical models from 305 MRI volumes , 1993, 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference.

[93]  Scott T. Grafton,et al.  Structural foundations of resting-state and task-based functional connectivity in the human brain , 2013, Proceedings of the National Academy of Sciences.

[94]  Karl J. Friston,et al.  PHRENOLOGY : What Can Neuroimaging Tell Us About Distributed Circuitry ? , 2005 .

[95]  Aaron S. Andalman,et al.  Structural and molecular interrogation of intact biological systems , 2013, Nature.