Functional brain network analysis using minimum spanning trees in Multiple Sclerosis: An MEG source-space study
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Cornelis J. Stam | Arjan Hillebrand | Frederik Barkhof | Prejaas Tewarie | Menno M. Schoonheim | Bob W. van Dijk | Jeroen J. G. Geurts | Chris Polman | C. Stam | B. Dijk | F. Barkhof | A. Hillebrand | M. Schoonheim | J. Geurts | C. Polman | P. Tewarie
[1] Jeffrey A. Cohen,et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. , 2014, Neurology.
[2] Yasheng Chen,et al. Diffusion tensor imaging based network analysis detects alterations of neuroconnectivity in patients with clinically early relapsing‐remitting multiple sclerosis , 2013, Human brain mapping.
[3] Michael Breakspear,et al. Graph analysis of the human connectome: Promise, progress, and pitfalls , 2013, NeuroImage.
[4] Cornelis J. Stam,et al. Cognitive and Clinical Dysfunction, Altered MEG Resting-State Networks and Thalamic Atrophy in Multiple Sclerosis , 2013, PloS one.
[5] Linda Douw,et al. Local MEG networks: The missing link between protein expression and epilepsy in glioma patients? , 2013, NeuroImage.
[6] C. Stam,et al. Cognition in MS correlates with resting-state oscillatory brain activity: An explorative MEG source-space study☆ , 2013, NeuroImage: Clinical.
[7] Cornelis J. Stam,et al. Growing Trees in Child Brains: Graph Theoretical Analysis of Electroencephalography-Derived Minimum Spanning Tree in 5- and 7-Year-Old Children Reflects Brain Maturation , 2013, Brain Connect..
[8] Cornelis J. Stam,et al. Structure out of chaos: Functional brain network analysis with EEG, MEG, and functional MRI , 2013, European Neuropsychopharmacology.
[9] Joachim Gross,et al. Good practice for conducting and reporting MEG research , 2013, NeuroImage.
[10] C. Stam,et al. Functional connectivity changes in multiple sclerosis patients: A graph analytical study of MEG resting state data , 2013, Human brain mapping.
[11] A. Hillebrand,et al. A three dimensional anatomical view of oscillatory resting-state activity and functional connectivity in Parkinson's disease related dementia: An MEG study using atlas-based beamforming☆ , 2012, NeuroImage: Clinical.
[12] A. Hillebrand,et al. Connectivity in MEG resting-state networks increases after resective surgery for low-grade glioma and correlates with improved cognitive performance☆ , 2012, NeuroImage: Clinical.
[13] H. Berendse,et al. Pathological organization of resting-state functional brain networks in Parkinson's disease: A longitudinal MEG graph theoretical analysis , 2013 .
[14] Linda Douw,et al. Subcortical atrophy and cognition , 2012, Neurology.
[15] D. Louis Collins,et al. Brain templates and atlases , 2012, NeuroImage.
[16] Cornelis J. Stam,et al. Cognitive Dysfunction in Early Multiple Sclerosis: Altered Centrality Derived from Resting-State Functional Connectivity Using Magneto-Encephalography , 2012, PloS one.
[17] M. Corbetta,et al. Large-scale cortical correlation structure of spontaneous oscillatory activity , 2012, Nature Neuroscience.
[18] C. Stam,et al. The organization of physiological brain networks , 2012, Clinical Neurophysiology.
[19] J. Aristotle. Betweenness Centrality , 2012 .
[20] O. Sporns,et al. The economy of brain network organization , 2012, Nature Reviews Neuroscience.
[21] Gareth R. Barnes,et al. Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution , 2012, NeuroImage.
[22] Frederik Barkhof,et al. Gender-related differences in functional connectivity in multiple sclerosis , 2012, Multiple sclerosis.
[23] James P. Bagrow,et al. Communities and bottlenecks: trees and treelike networks have high modularity. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.
[24] Petr Bob,et al. The temporal organization of functional brain connectivity is abnormal in schizophrenia but does not correlate with symptomatology , 2011, Consciousness and Cognition.
[25] Yong He,et al. Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis. , 2011, Cerebral cortex.
[26] J. Brewer,et al. Default network correlations analyzed on native surfaces , 2011, Journal of Neuroscience Methods.
[27] J. Palva,et al. Functional Roles of Alpha-Band Phase Synchronization in Local and Large-Scale Cortical Networks , 2011, Front. Psychology.
[28] K. Çiftçi,et al. Minimum Spanning Tree Reflects the Alterations of the Default Mode Network During Alzheimer’s Disease , 2011, Annals of Biomedical Engineering.
[29] Jeffrey A. Cohen,et al. Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria , 2011, Annals of neurology.
[30] Nikolaus Weiskopf,et al. Unified segmentation based correction of R1 brain maps for RF transmit field inhomogeneities (UNICORT) , 2011, NeuroImage.
[31] Andreas Daffertshofer,et al. Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory , 2010, PloS one.
[32] Olaf Sporns,et al. Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.
[33] Edward T. Bullmore,et al. Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .
[34] Linda Douw,et al. Human Neuroscience , 2022 .
[35] J. Matias Palva,et al. Graph properties of synchronized cortical networks during visual working memory maintenance , 2010, NeuroImage.
[36] T. S. Jackson,et al. Theory of minimum spanning trees. II. Exact graphical methods and perturbation expansion at the percolation threshold. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.
[37] T. S. Jackson,et al. Theory of minimum spanning trees. I. Mean-field theory and strongly disordered spin-glass model. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.
[38] Linda Douw,et al. Functional connectivity in the brain before and during intra-arterial amobarbital injection (Wada test) , 2009, NeuroImage.
[39] Alan C. Evans,et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. , 2009, Cerebral cortex.
[40] G. Sandini,et al. Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. , 2009, Brain : a journal of neurology.
[41] Guillermo J. Ortega,et al. Complex network analysis of human ECoG data , 2008, Neuroscience Letters.
[42] Linda Douw,et al. Treatment-related changes in functional connectivity in brain tumor patients: A magnetoencephalography study , 2008, Experimental Neurology.
[43] Javier Martín Hernández,et al. Betweenness centrality in a weighted network. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.
[44] Matthew J. Brookes,et al. Optimising experimental design for MEG beamformer imaging , 2008, NeuroImage.
[45] C. J. Stam,et al. The influence of low-grade glioma on resting state oscillatory brain activity: a magnetoencephalography study , 2008, Journal of Neuro-Oncology.
[46] Stefano Battiston,et al. Systemic risk in a network fragility model analyzed with probability density evolution of persistent random walks , 2007, Networks Heterog. Media.
[47] C. Stam,et al. Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.
[48] H. Stanley,et al. Optimal Path and Minimal Spanning Trees in Random Weighted Networks , 2006, Int. J. Bifurc. Chaos.
[49] Cornelis J. Stam,et al. Magnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer's disease , 2006, NeuroImage.
[50] C. Stam,et al. Disturbed functional connectivity in brain tumour patients: Evaluation by graph analysis of synchronization matrices , 2006, Clinical Neurophysiology.
[51] Ki-Young Jung,et al. Classification of epilepsy types through global network analysis of scalp electroencephalograms. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.
[52] S. Reingold,et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria” , 2005, Annals of neurology.
[53] Piet Van Mieghem,et al. Phase transition in the link weight structure of networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.
[54] Krish D. Singh,et al. A new approach to neuroimaging with magnetoencephalography , 2005, Human brain mapping.
[55] P. Van Mieghem,et al. Influence of the link weight structure on the shortest path , 2005 .
[56] Arjan Hillebrand,et al. Beamformer analysis of MEG data. , 2005, International review of neurobiology.
[57] G. Barnes,et al. Realistic spatial sampling for MEG beamformer images , 2004, Human brain mapping.
[58] C. J. Stam,et al. Functional connectivity patterns of human magnetoencephalographic recordings: a ‘small-world’ network? , 2004, Neuroscience Letters.
[59] G. Barnes,et al. Statistical flattening of MEG beamformer images , 2003, Human brain mapping.
[60] Stephen M. Smith,et al. Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.
[61] G. R. Barnes,et al. A Quantitative Assessment of the Sensitivity of Whole-Head MEG to Activity in the Adult Human Cortex , 2002, NeuroImage.
[62] N. Tzourio-Mazoyer,et al. Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.
[63] Thomas E. Nichols,et al. Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.
[64] A. von Stein,et al. Different frequencies for different scales of cortical integration: from local gamma to long range alpha/theta synchronization. , 2000, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[65] W. Klimesch. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.
[66] Se Robinson,et al. Functional neuroimaging by Synthetic Aperture Magnetometry (SAM) , 1999 .
[67] Duncan J. Watts,et al. Collective dynamics of ‘small-world’ networks , 1998, Nature.
[68] S. Reingold,et al. Defining the clinical course of multiple sclerosis , 1996, Neurology.
[69] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[70] Karl J. Friston,et al. Statistical parametric maps in functional imaging: A general linear approach , 1994 .
[71] HERBERT A. SIMON,et al. The Architecture of Complexity , 1991 .
[72] J. Kurtzke. Rating neurologic impairment in multiple sclerosis , 1983, Neurology.
[73] J. Kruskal. On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .