Network Localisation of White Matter Damage in Cerebral Small Vessel Disease
暂无分享,去创建一个
Bastian Cheng | Götz Thomalla | Jens Fiehler | Christian Gerloff | Marvin Petersen | Uta Hanning | Eckhard Schlemm | Benedikt M. Frey | Carola Mayer | Kristin Engelke | Katrin Borof | Annika Jagodzinski
[1] Sandra E. Black,et al. Associations between amyloid β and white matter hyperintensities: A systematic review , 2017, Alzheimer's & Dementia.
[2] Stamatios N. Sotiropoulos,et al. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging , 2016, NeuroImage.
[3] Andrew Zalesky,et al. Building connectomes using diffusion MRI: why, how and but , 2017, NMR in biomedicine.
[4] Michael Wagner,et al. A peripheral epigenetic signature of immune system genes is linked to neocortical thickness and memory , 2017, Nature Communications.
[5] H. Markus,et al. Longitudinal patterns of leukoaraiosis and brain atrophy in symptomatic small vessel disease , 2016, Brain : a journal of neurology.
[6] P. Sachdev,et al. Are the brain's vascular and Alzheimer pathologies additive or interactive? , 2017, Current opinion in psychiatry.
[7] Owen Carmichael,et al. White Matter Hyperintensity Penumbra , 2011, Stroke.
[8] F. de Leeuw,et al. A Novel Imaging Marker for Small Vessel Disease Based on Skeletonization of White Matter Tracts and Diffusion Histograms , 2016, Annals of neurology.
[9] M. Delgado-Rodríguez,et al. Systematic review and meta-analysis. , 2017, Medicina intensiva.
[10] Edward T. Bullmore,et al. Connectomics: A new paradigm for understanding brain disease , 2015, European Neuropsychopharmacology.
[11] David G. Norris,et al. Relationship Between White Matter Hyperintensities, Cortical Thickness, and Cognition , 2015, Stroke.
[12] Alan Connelly,et al. The effects of SIFT on the reproducibility and biological accuracy of the structural connectome , 2015, NeuroImage.
[13] D. Bennett,et al. White matter changes: neurobehavioral manifestations of Binswanger's disease and clinical correlates in Alzheimer's disease. , 1994, Dementia.
[14] R. G. Lacsamana,et al. Where do we go from here? , 1986, The Journal of the Florida Medical Association.
[15] Alan Connelly,et al. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution , 2004, NeuroImage.
[16] Alan Connelly,et al. SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography , 2015, NeuroImage.
[17] David G Norris,et al. Diffusion tensor imaging and cognition in cerebral small vessel disease: the RUN DMC study. , 2012, Biochimica et biophysica acta.
[18] Duncan J. Watts,et al. Collective dynamics of ‘small-world’ networks , 1998, Nature.
[19] Johan Svensson,et al. Update on Vascular Cognitive Impairment Associated with Subcortical Small-Vessel Disease2 , 2018, Journal of Alzheimer's disease : JAD.
[20] H. Chui,et al. Subcortical ischaemic vascular dementia , 2002, The Lancet Neurology.
[21] B. Cheng,et al. Characterization of White Matter Hyperintensities in Large-Scale MRI-Studies , 2019, Front. Neurol..
[22] Stephen M. Smith,et al. A Bayesian model of shape and appearance for subcortical brain segmentation , 2011, NeuroImage.
[23] Nick C Fox,et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration , 2013, The Lancet Neurology.
[24] Stephen T. C. Wong,et al. Cortical and frontal atrophy are associated with cognitive impairment in age-related confluent white-matter lesion , 2010, Journal of Neurology, Neurosurgery & Psychiatry.
[25] Alan Connelly,et al. Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information , 2012, NeuroImage.
[26] E. Bullmore,et al. Impaired long distance functional connectivity and weighted network architecture in Alzheimer's disease. , 2014, Cerebral cortex.
[27] A. Wallin. The Overlap between Alzheimer’s Disease and Vascular Dementia: The Role of White Matter Changes , 1998, Dementia and Geriatric Cognitive Disorders.
[28] D. Norris,et al. Disruption of rich club organisation in cerebral small vessel disease , 2016, Human brain mapping.
[29] R. Kahn,et al. Efficiency of Functional Brain Networks and Intellectual Performance , 2009, The Journal of Neuroscience.
[30] Chun-Hung Yeh,et al. Is removal of weak connections necessary for graph-theoretical analysis of dense weighted structural connectomes from diffusion MRI? , 2019, NeuroImage.
[31] K. Jellinger,et al. The overlap between vascular disease and Alzheimer’s disease - lessons from pathology , 2014, BMC Medicine.
[32] Bastian Cheng,et al. Altered topology of large-scale structural brain networks in chronic stroke , 2019, Brain communications.
[33] C. Büchel,et al. Rationale and Design of the Hamburg City Health Study , 2019, European Journal of Epidemiology.
[34] H. Markus,et al. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis , 2010, BMJ : British Medical Journal.
[35] 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.
[36] H. C. Chui,et al. White matter lesions impair frontal lobe function regardless of their location , 2004, Neurology.
[37] Bibek Dhital,et al. Gibbs‐ringing artifact removal based on local subvoxel‐shifts , 2015, Magnetic resonance in medicine.
[38] Alan Connelly,et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation , 2019, NeuroImage.
[39] Chun-Hung Yeh,et al. Is removal of weak connections necessary for graph-theoretical analysis of dense weighted structural connectomes? , 2019, bioRxiv.
[40] Bastian Cheng,et al. Cortical atrophy and transcallosal diaschisis following isolated subcortical stroke , 2020, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.
[41] Daniel Rueckert,et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.
[42] Danielle S Bassett,et al. Cognitive fitness of cost-efficient brain functional networks , 2009, Proceedings of the National Academy of Sciences.
[43] F.‐E. Leeuw,et al. Cerebral small vessel disease: from a focal to a global perspective , 2018, Nature Reviews Neurology.
[44] Brian B. Avants,et al. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..
[45] Heidi Johansen-Berg,et al. Tractography: Where Do We Go from Here? , 2011, Brain Connect..
[46] Andrew Simmons,et al. Beyond cortical localization in clinico-anatomical correlation , 2012, Cortex.
[47] L. Pantoni. Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges , 2010, The Lancet Neurology.
[48] Stephen M Smith,et al. Fast robust automated brain extraction , 2002, Human brain mapping.
[49] Lenore J. Launer,et al. Cerebral small vessel disease and risk of incident stroke, dementia and depression, and all-cause mortality: A systematic review and meta-analysis , 2018, Neuroscience & Biobehavioral Reviews.
[50] A. W. Chung,et al. Structural network efficiency is associated with cognitive impairment in small-vessel disease , 2014, Neurology.
[51] Reinhold Schmidt,et al. Strategic white matter tracts for processing speed deficits in age-related small vessel disease , 2014, Neurology.
[52] Chun-Hung Yeh,et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation , 2019, NeuroImage.
[53] E. Mohammadi,et al. Barriers and facilitators related to the implementation of a physiological track and trigger system: A systematic review of the qualitative evidence , 2017, International journal for quality in health care : journal of the International Society for Quality in Health Care.
[54] Benjamin S. Aribisala,et al. White matter hyperintensities and normal-appearing white matter integrity in the aging brain , 2015, Neurobiology of Aging.
[55] Wim Fias,et al. Brain networks under attack: robustness properties and the impact of lesions. , 2016, Brain : a journal of neurology.
[56] Ludovica Griffanti,et al. BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities , 2016, NeuroImage.
[57] P. Matthews,et al. White matter lesion progression, brain atrophy, and cognitive decline: The Austrian stroke prevention study , 2005, Annals of neurology.
[58] A. Connelly,et al. Improved probabilistic streamlines tractography by 2 nd order integration over fibre orientation distributions , 2009 .
[59] Peter F. Neher,et al. The challenge of mapping the human connectome based on diffusion tractography , 2017, Nature Communications.
[60] Seth Love,et al. Cerebrovascular disease in ageing and Alzheimer’s disease , 2015, Acta Neuropathologica.
[61] 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.
[62] Jan Sijbers,et al. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data , 2014, NeuroImage.
[63] Jan Sijbers,et al. Denoising of diffusion MRI using random matrix theory , 2016, NeuroImage.
[64] Justin P. Haldar,et al. Co-registration and distortion correction of diffusion and anatomical images based on inverse contrast normalization , 2015, NeuroImage.