MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI

Diffusion‐weighted imaging allows investigators to identify structural, microstructural, and connectivity‐based differences between subjects, but variability due to session and scanner biases is a challenge.

[1]  Derek K. Jones,et al.  MICRA: Microstructural image compilation with repeated acquisitions , 2020, NeuroImage.

[2]  Christophe Lenglet,et al.  Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset? , 2020, NeuroImage.

[3]  Benjamin N. Conrad,et al.  PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images , 2020, bioRxiv.

[4]  Carlo Pierpaoli,et al.  Brain connections derived from diffusion MRI tractography can be highly anatomically accurate—if we know where white matter pathways start, where they end, and where they do not go , 2020, Brain Structure and Function.

[5]  Qi Yang,et al.  Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography , 2020, Neuroinformatics.

[6]  Peipeng Liang,et al.  Multicenter dataset of multi-shell diffusion MRI in healthy traveling adults with identical settings , 2020, Scientific Data.

[7]  Stamatios N. Sotiropoulos,et al.  XTRACT - Standardised protocols for automated tractography in the human and macaque brain , 2020, NeuroImage.

[8]  Fang-Cheng Yeh,et al.  Shape analysis of the human association pathways , 2020, NeuroImage.

[9]  Bennett A Landman,et al.  Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps , 2020, bioRxiv.

[10]  Gabriel Girard,et al.  Tractostorm: The what, why, and how of tractography dissection reproducibility , 2020, Human brain mapping.

[11]  S. Aoki,et al.  Scan–rescan and inter-vendor reproducibility of neurite orientation dispersion and density imaging metrics , 2019, Neuroradiology.

[12]  Yogesh Rathi,et al.  White matter abnormalities across the lifespan of schizophrenia: a harmonized multi-site diffusion MRI study , 2019, Molecular Psychiatry.

[13]  Sergiu Groppa,et al.  Characterizing Microstructural Tissue Properties in Multiple Sclerosis with Diffusion MRI at 7 T and 3 T: The Impact of the Experimental Design , 2019, Neuroscience.

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

[15]  Wilfried Philips,et al.  Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks , 2019, Medical Image Anal..

[16]  A. Desautels,et al.  Test-Retest Reliability of Diffusion Measures Extracted Along White Matter Language Fiber Bundles Using HARDI-Based Tractography , 2019, Front. Neurosci..

[17]  V. Arolt,et al.  Variation of HbA1c affects cognition and white matter microstructure in healthy, young adults , 2018, bioRxiv.

[18]  Joseph V. Hajnal,et al.  Complex diffusion-weighted image estimation via matrix recovery under general noise models , 2018, NeuroImage.

[19]  Peter F. Neher,et al.  Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge , 2018, bioRxiv.

[20]  Maxime Descoteaux,et al.  The Canadian Dementia Imaging Protocol: Harmonizing National Cohorts , 2018, Journal of magnetic resonance imaging : JMRI.

[21]  Essa Yacoub,et al.  The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5–21 year olds , 2018, NeuroImage.

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

[23]  Anders M. Dale,et al.  The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites , 2018, Developmental Cognitive Neuroscience.

[24]  Scott T. Grafton,et al.  A probabilistic atlas of fiber crossings for variability reduction of anisotropy measures , 2018, Brain Structure and Function.

[25]  Felix C. Morency,et al.  A test-retest study on Parkinson's PPMI dataset yields statistically significant white matter fascicles , 2017, NeuroImage: Clinical.

[26]  Maxime Descoteaux,et al.  Recognition of white matter bundles using local and global streamline-based registration and clustering , 2017, NeuroImage.

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

[28]  Susan M. Courtney,et al.  Probabilistic White Matter Atlases of Human Auditory, Basal Ganglia, Language, Precuneus, Sensorimotor, Visual and Visuospatial Networks , 2017, Frontiers in human neuroscience.

[29]  Martijn Froeling,et al.  “MASSIVE” brain dataset: Multiple acquisitions for standardization of structural imaging validation and evaluation , 2017, Magnetic resonance in medicine.

[30]  Daniel P. Kennedy,et al.  Enhancing studies of the connectome in autism using the autism brain imaging data exchange II , 2017, Scientific Data.

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

[32]  Stamatios N. Sotiropoulos,et al.  Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images , 2016, NeuroImage.

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

[34]  Satrajit S. Ghosh,et al.  The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments , 2016, Scientific Data.

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

[36]  Rachid Deriche,et al.  Studying white matter tractography reproducibility through connectivity matrices , 2015 .

[37]  D. Auer,et al.  Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain , 2015, NMR in biomedicine.

[38]  Arno Klein,et al.  Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements , 2014, NeuroImage.

[39]  鎌形康司.  高分解能Neurite Orientation Dispersion and Density Imagingを用いたパーキンソン病黒質変性の評価 , 2014 .

[40]  Maxime Descoteaux,et al.  Dipy, a library for the analysis of diffusion MRI data , 2014, Front. Neuroinform..

[41]  Timothy D. Verstynen,et al.  Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy , 2013, PloS one.

[42]  Jan Sijbers,et al.  Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls , 2013, NeuroImage.

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

[44]  Alan Connelly,et al.  SIFT: Spherical-deconvolution informed filtering of tractograms , 2013, NeuroImage.

[45]  Jessica A. Turner,et al.  MultiCenter Reliability of Diffusion Tensor Imaging , 2012, Brain Connect..

[46]  Do P. M. Tromp,et al.  Diffusion Tensor Imaging in Autism Spectrum Disorder: A Review , 2012, Autism research : official journal of the International Society for Autism Research.

[47]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[48]  P. Hofman,et al.  Tract Specific Reproducibility of Tractography Based Morphology and Diffusion Metrics , 2012, PloS one.

[49]  Mark F. Lythgoe,et al.  Compartment models of the diffusion MR signal in brain white matter: A taxonomy and comparison , 2012, NeuroImage.

[50]  Timothy Edward John Behrens,et al.  Automated Probabilistic Reconstruction of White-Matter Pathways in Health and Disease Using an Atlas of the Underlying Anatomy , 2011, Front. Neuroinform..

[51]  C. Westin,et al.  An introduction to diffusion tensor image analysis. , 2011, Neurosurgery clinics of North America.

[52]  M. Belke,et al.  Men and women are different: Diffusion tensor imaging reveals sexual dimorphism in the microstructure of the thalamus, corpus callosum and cingulum , 2011, NeuroImage.

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

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

[55]  John S. Duncan,et al.  Identical, but not the same: Intra-site and inter-site reproducibility of fractional anisotropy measures on two 3.0 T scanners , 2010, NeuroImage.

[56]  B. Mueller,et al.  Diffusion Tensor Imaging Identifies Deficits in White Matter Microstructure in Subjects With Type 1 Diabetes That Correlate With Reduced Neurocognitive Function , 2008, Diabetes.

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

[58]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

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

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

[61]  Alan Connelly,et al.  Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution , 2007, NeuroImage.

[62]  N. Makris,et al.  Hypothalamic Abnormalities in Schizophrenia: Sex Effects and Genetic Vulnerability , 2007, Biological Psychiatry.

[63]  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.

[64]  N. Makris,et al.  Decreased volume of left and total anterior insular lobule in schizophrenia , 2006, Schizophrenia Research.

[65]  P. Szeszko,et al.  MRI atlas of human white matter , 2006 .

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

[67]  S. Rauch,et al.  Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder. , 2005, The American journal of psychiatry.

[68]  Derek K. Jones,et al.  RESTORE: Robust estimation of tensors by outlier rejection , 2005, Magnetic resonance in medicine.

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

[70]  A. Szafer,et al.  An analytical model of restricted diffusion in bovine optic nerve , 1997, Magnetic resonance in medicine.

[71]  Philip H. Ramsey.  Nonparametric Statistical Methods , 1974, Technometrics.

[72]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[73]  Pablo Villoslada,et al.  Reproducibility of the Structural Connectome Reconstruction across Diffusion Methods , 2016, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[74]  Cam Wheeler-Kingshott,et al.  Assessing scan-rescan reproducibility of the parameter estimates from NODDI , 2013 .

[75]  Y. Assaf,et al.  Diffusion Tensor Imaging (DTI)-based White Matter Mapping in Brain Research: A Review , 2007, Journal of Molecular Neuroscience.

[76]  Daniel C. Alexander,et al.  Camino: Open-Source Diffusion-MRI Reconstruction and Processing , 2006 .

[77]  P. Basser,et al.  MR diffusion tensor spectroscopy and imaging. , 1994, Biophysical journal.