A new method for accurate in vivo mapping of human brain connections using microstructural and anatomical information
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Jean-Philippe Thiran | Mario Ocampo-Pineda | Simona Schiavi | Muhamed Barakovic | Maxime Descoteaux | Alessandro Daducci | Laurent Petit | J. Thiran | L. Petit | M. Descoteaux | Alessandro Daducci | S. Schiavi | Mario Ocampo-Pineda | M. Barakovic
[1] Hansheng Wang,et al. Computational Statistics and Data Analysis a Note on Adaptive Group Lasso , 2022 .
[2] P. Basser,et al. In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.
[3] Kotagiri Ramamohanarao,et al. Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography? , 2018, Magnetic resonance in medicine.
[4] Thomas R. Knösche,et al. White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI , 2013, NeuroImage.
[5] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[6] Essa Yacoub,et al. The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.
[7] Alan Connelly,et al. The effects of SIFT on the reproducibility and biological accuracy of the structural connectome , 2015, NeuroImage.
[8] Olaf Sporns,et al. The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..
[9] Leonardo L. Gollo,et al. Connectome sensitivity or specificity: which is more important? , 2016, NeuroImage.
[10] Maxime Descoteaux,et al. Tractometer: Towards validation of tractography pipelines , 2013, Medical Image Anal..
[11] Alessandro Daducci,et al. Microstructure Informed Tractography: Pitfalls and Open Challenges , 2016, Front. Neurosci..
[12] V. Kiselev,et al. Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation , 2016, NMR in biomedicine.
[13] Carl-Fredrik Westin,et al. Fiber clustering versus the parcellation-based connectome , 2013, NeuroImage.
[14] Alan Connelly,et al. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution , 2007, NeuroImage.
[15] Alan Connelly,et al. SIFT: Spherical-deconvolution informed filtering of tractograms , 2013, NeuroImage.
[16] Stephen M. Smith,et al. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.
[17] Alan Connelly,et al. SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography , 2015, NeuroImage.
[18] Jan Sijbers,et al. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data , 2014, NeuroImage.
[19] Yaniv Assaf,et al. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain , 2005, NeuroImage.
[20] D. Leopold,et al. Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited , 2014, Proceedings of the National Academy of Sciences.
[21] Danielle S Bassett,et al. Brain graphs: graphical models of the human brain connectome. , 2011, Annual review of clinical psychology.
[22] Hui Zhang,et al. Imaging brain microstructure with diffusion MRI: practicality and applications , 2019, NMR in biomedicine.
[23] Daniel C. Alexander,et al. Multi-compartment microscopic diffusion imaging , 2016, NeuroImage.
[24] Jean-Philippe Thiran,et al. Structural connectomics in brain diseases , 2013, NeuroImage.
[25] Derek K. Jones,et al. Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data , 2015, NeuroImage.
[26] J. Rilling,et al. Comparison of diffusion tractography and tract‐tracing measures of connectivity strength in rhesus macaque connectome , 2015, Human brain mapping.
[27] 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.
[28] F. Pestilli,et al. Evaluation and statistical inference for living connectomes , 2014, Nature Methods.
[29] Alan Connelly,et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation , 2019, NeuroImage.
[30] Daniel C. Alexander,et al. NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.
[31] Jean-Francois Mangin,et al. Comparison between diffusion MRI tractography and histological tract-tracing of cortico-cortical structural connectivity in the ferret brain , 2019, bioRxiv.
[32] Mark F. Lythgoe,et al. Compartment models of the diffusion MR signal in brain white matter: A taxonomy and comparison , 2012, NeuroImage.
[33] Peter F. Neher,et al. The challenge of mapping the human connectome based on diffusion tractography , 2017, Nature Communications.
[34] Jean-Philippe Thiran,et al. COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography , 2015, IEEE Transactions on Medical Imaging.
[35] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[36] O. Sporns,et al. The economy of brain network organization , 2012, Nature Reviews Neuroscience.
[37] Alan Connelly,et al. Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information , 2012, NeuroImage.
[38] E. Bullmore,et al. Human brain networks in health and disease , 2009, Current opinion in neurology.
[39] Peter F. Neher,et al. Limits to anatomical accuracy of diffusion tractography using modern approaches , 2018, NeuroImage.