Using The Virtual Brain to study the relationship between structural and functional connectivity in patients with multiple sclerosis: a multicenter study.
暂无分享,去创建一个
G. Deco | M. Schoonheim | A. Toosy | H. Harbo | P. Tewarie | J. Sastre-Garriga | M. Rocca | D. Pareto | G. González-Escamilla | E. Martínez-Heras | S. Llufriu | S. Groppa | E. Høgestøl | M. Petracca | Á. Vidal-Jordana | G. Pontillo | Gerard Martí-Juan | M. Filippi | À. Rovira | M. Foster
[1] M. Filippi,et al. The role of cerebellar damage in explaining disability and cognition in multiple sclerosis phenotypes: a multiparametric MRI study , 2022, Journal of Neurology.
[2] A. McIntosh,et al. Personalized Connectome-Based Modeling in Patients with Semi-Acute Phase TBI: Relationship to Acute Neuroimaging and 6 Month Follow-Up , 2022, eNeuro.
[3] S. Elledge,et al. Longitudinal analysis reveals high prevalence of Epstein-Barr virus associated with multiple sclerosis , 2022, Science.
[4] E. D’Angelo,et al. Subject-specific features of excitation/inhibition profiles in neurodegenerative diseases , 2021, bioRxiv.
[5] M. Reuter,et al. FastSurferVINN: Building resolution-independence into deep learning segmentation methods—A solution for HighRes brain MRI , 2021, NeuroImage.
[6] O. Ciccarelli,et al. Spatial patterns of brain lesions assessed through covariance estimations of lesional voxels in multiple Sclerosis: The SPACE-MS technique , 2021, NeuroImage: Clinical.
[7] O. Ciccarelli,et al. Linking immune-mediated damage to neurodegeneration in multiple sclerosis: could network-based MRI help? , 2021, Brain communications.
[8] J. Jovicich,et al. Structural Brain Network Reproducibility: Influence of Different Diffusion Acquisition and Tractography Reconstruction Schemes on Graph Metrics , 2021, Brain Connect..
[9] À. Rovira,et al. Beyond McDonald: updated perspectives on MRI diagnosis of multiple sclerosis , 2021, Expert review of neurotherapeutics.
[10] O. Ciccarelli,et al. 2021 MAGNIMS–CMSC–NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis , 2021, The Lancet Neurology.
[11] Leonardo L. Gollo,et al. On the intersection between data quality and dynamical modelling of large-scale fMRI signals , 2021, NeuroImage.
[12] O. Ciccarelli,et al. Mind the gap: from neurons to networks to outcomes in multiple sclerosis , 2021, Nature Reviews Neurology.
[13] E. Leray,et al. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition , 2020, Multiple sclerosis.
[14] Petra Ritter,et al. The Importance of Cerebellar Connectivity on Simulated Brain Dynamics , 2020, Frontiers in Cellular Neuroscience.
[15] O. Ciccarelli,et al. Reduced dynamics of functional connectivity and cognitive impairment in multiple sclerosis , 2020, Multiple sclerosis.
[16] M. Battaglini,et al. MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice , 2020, Nature Reviews Neurology.
[17] Christian Wachinger,et al. Detect and Correct Bias in Multi-Site Neuroimaging Datasets , 2020, Medical Image Anal..
[18] B. Fischl,et al. FastSurfer - A fast and accurate deep learning based neuroimaging pipeline , 2019, NeuroImage.
[19] Daniele Marinazzo,et al. Modeling brain dynamics after tumor resection using The Virtual Brain , 2019, NeuroImage.
[20] Viktor K. Jirsa,et al. Transmission time delays organize the brain network synchronization , 2019, Philosophical Transactions of the Royal Society A.
[21] Chun-Hung Yeh,et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation , 2019, NeuroImage.
[22] Jaume Sastre-Garriga,et al. Treatment of multiple sclerosis — success from bench to bedside , 2018, Nature Reviews Neurology.
[23] M. Filippi,et al. Functional network connectivity abnormalities in multiple sclerosis: Correlations with disability and cognitive impairment , 2018, Multiple sclerosis.
[24] J. Zimmermann,et al. Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models , 2018, NeuroImage: Clinical.
[25] C. Stam,et al. Explaining the heterogeneity of functional connectivity findings in multiple sclerosis: An empirically informed modeling study , 2018, Human brain mapping.
[26] David H. Miller,et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria , 2017, The Lancet Neurology.
[27] Vinzenz Fleischer,et al. Graph Theoretical Framework of Brain Networks in Multiple Sclerosis: A Review of Concepts , 2017, Neuroscience.
[28] Morten L. Kringelbach,et al. Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms , 2017, NeuroImage.
[29] Gustavo Deco,et al. Inferring multi-scale neural mechanisms with brain network modelling , 2017, bioRxiv.
[30] Carl D. Hacker,et al. Decreased integration and information capacity in stroke measured by whole brain models of resting state activity , 2017, Brain : a journal of neurology.
[31] M. Schoonheim. Functional reorganization is a maladaptive response to injury – Commentary , 2017, Multiple sclerosis.
[32] Viktor K. Jirsa,et al. The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread , 2017, NeuroImage.
[33] Gustavo Deco,et al. The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core , 2016, bioRxiv.
[34] Sara Llufriu,et al. Structural networks involved in attention and executive functions in multiple sclerosis , 2016, NeuroImage: Clinical.
[35] Viktor K. Jirsa,et al. How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models? , 2016, NeuroImage.
[36] Anthony R. McIntosh,et al. Functional Mechanisms of Recovery after Chronic Stroke: Modeling with the Virtual Brain123 , 2016, eNeuro.
[37] F. X. Aymerich,et al. Lesion filling effect in regional brain volume estimations: a study in multiple sclerosis patients with low lesion load , 2016, Neuroradiology.
[38] Stamatios N. Sotiropoulos,et al. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging , 2016, NeuroImage.
[39] Alan Connelly,et al. SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography , 2015, NeuroImage.
[40] Sara Llufriu,et al. Improved Framework for Tractography Reconstruction of the Optic Radiation , 2015, PloS one.
[41] F. Barkhof,et al. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis—clinical implementation in the diagnostic process , 2015, Nature Reviews Neurology.
[42] G. Tononi,et al. Rethinking segregation and integration: contributions of whole-brain modelling , 2015, Nature Reviews Neuroscience.
[43] Viktor K. Jirsa,et al. Mathematical framework for large-scale brain network modeling in The Virtual Brain , 2015, NeuroImage.
[44] Menno M. Schoonheim,et al. Network Collapse and Cognitive Impairment in Multiple Sclerosis , 2015, Front. Neurol..
[45] Gustavo Deco,et al. Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-time interaction , 2015, Front. Neuroinform..
[46] Bibek Dhital,et al. Gibbs‐ringing artifact removal based on local subvoxel‐shifts , 2015, Magnetic resonance in medicine.
[47] D. Collins,et al. The Role of the Cerebellum in Multiple Sclerosis , 2015, The Cerebellum.
[48] Jan Sijbers,et al. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data , 2014, NeuroImage.
[49] G. Nagels,et al. The Symbol Digit Modalities Test as sentinel test for cognitive impairment in multiple sclerosis , 2014, European journal of neurology.
[50] M. Corbetta,et al. How Local Excitation–Inhibition Ratio Impacts the Whole Brain Dynamics , 2014, The Journal of Neuroscience.
[51] Hamid Reza Mohseni,et al. How delays matter in an oscillatory whole-brain spiking-neuron network model for MEG alpha-rhythms at rest , 2014, NeuroImage.
[52] B. Stankoff,et al. Demyelination in multiple sclerosis , 2014, Handbook of Clinical Neurology.
[53] Viktor K. Jirsa,et al. The Virtual Brain: a simulator of primate brain network dynamics , 2013, Front. Neuroinform..
[54] E. Leray,et al. ‘Clinically definite benign multiple sclerosis’, an unwarranted conceptual hodgepodge: evidence from a 30-year observational study , 2013, Multiple sclerosis.
[55] Alan Connelly,et al. Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information , 2012, NeuroImage.
[56] Susan L. Whitfield-Gabrieli,et al. Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks , 2012, Brain Connect..
[57] Bernhard Hemmer,et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis , 2012, NeuroImage.
[58] O. Sporns,et al. Key role of coupling, delay, and noise in resting brain fluctuations , 2009, Proceedings of the National Academy of Sciences.
[59] Viktor K. Jirsa,et al. Noise during Rest Enables the Exploration of the Brain's Dynamic Repertoire , 2008, PLoS Comput. Biol..
[60] Alan Connelly,et al. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution , 2007, NeuroImage.
[61] Xiao-Jing Wang,et al. A Recurrent Network Mechanism of Time Integration in Perceptual Decisions , 2006, The Journal of Neuroscience.
[62] Hans Lassmann,et al. Cortical demyelination and diffuse white matter injury in multiple sclerosis. , 2005, Brain : a journal of neurology.
[63] Mark W. Woolrich,et al. Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.
[64] Karl J. Friston,et al. Dynamic causal modelling , 2003, NeuroImage.
[65] Mark Jenkinson,et al. Fast, automated, N‐dimensional phase‐unwrapping algorithm , 2003, Magnetic resonance in medicine.
[66] G. Comi,et al. Mean diffusivity and fractional anisotropy histograms of patients with multiple sclerosis. , 2001, AJNR. American journal of neuroradiology.
[67] Karl J. Friston,et al. Nonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics , 2000, NeuroImage.
[68] 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.
[69] A. Connelly,et al. Improved probabilistic streamlines tractography by 2 nd order integration over fibre orientation distributions , 2009 .
[70] W. L. Benedict,et al. Multiple Sclerosis , 2007, Journal - Michigan State Medical Society.