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P Tewarie

Showing results (1-10 of 14) with videos related to

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Neuroimage|December 3, 2014
The minimum spanning tree: an unbiased method for brain network analysisP Tewarie, E van Dellen, A Hillebrand, et al.
Neuroimage|February 10, 2017
The epidemic spreading model and the direction of information flow in brain networksJ Meier, X Zhou, A Hillebrand, et al.
Multiple Sclerosis (Houndmills, Basingstoke, England)|January 10, 2014
Disease course heterogeneity and OCT in multiple sclerosisL J Balk, P Tewarie, J Killestein, et al.
International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology|April 15, 2014
The trees and the forest: Characterization of complex brain networks with minimum spanning treesC J Stam, P Tewarie, E Van Dellen, et al.
Neuroimage. Clinical|November 2, 2013
Cognition in MS correlates with resting-state oscillatory brain activity: An explorative MEG source-space studyM L Van der Meer, P Tewarie, M M Schoonheim, et al.
International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology|February 14, 2015
The relation between structural and functional connectivity patterns in complex brain networksC J Stam, E C W van Straaten, E Van Dellen, et al.
Neuroimage|October 29, 2013
Functional brain network analysis using minimum spanning trees in Multiple Sclerosis: an MEG source-space studyP Tewarie, A Hillebrand, M M Schoonheim, et al.
Journal of Neuroimmunology|August 4, 2012
Cerebrospinal fluid anti-whole myelin antibodies are not correlated to magnetic resonance imaging activity in multiple sclerosisP Tewarie, C E Teunissen, C D Dijkstra, et al.
Network Neuroscience (Cambridge, Mass.)|June 23, 2022
Minimum spanning tree analysis of brain networks: A systematic review of network size effects, sensitivity for neuropsychiatric pathology, and disorder specificityN Blomsma, B de Rooy, F Gerritse, et al.
Neuroimage|February 2, 2016
Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactionsP Tewarie, M G Bright, A Hillebrand, et al.
Pageof 2

Showing results (1-10 of 14) with videos related to

Sort By:
Pageof 2
Neuroimage|December 3, 2014
The minimum spanning tree: an unbiased method for brain network analysisP Tewarie, E van Dellen, A Hillebrand, et al.
Neuroimage|February 10, 2017
The epidemic spreading model and the direction of information flow in brain networksJ Meier, X Zhou, A Hillebrand, et al.
Multiple Sclerosis (Houndmills, Basingstoke, England)|January 10, 2014
Disease course heterogeneity and OCT in multiple sclerosisL J Balk, P Tewarie, J Killestein, et al.
International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology|April 15, 2014
The trees and the forest: Characterization of complex brain networks with minimum spanning treesC J Stam, P Tewarie, E Van Dellen, et al.
Neuroimage. Clinical|November 2, 2013
Cognition in MS correlates with resting-state oscillatory brain activity: An explorative MEG source-space studyM L Van der Meer, P Tewarie, M M Schoonheim, et al.
International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology|February 14, 2015
The relation between structural and functional connectivity patterns in complex brain networksC J Stam, E C W van Straaten, E Van Dellen, et al.
Neuroimage|October 29, 2013
Functional brain network analysis using minimum spanning trees in Multiple Sclerosis: an MEG source-space studyP Tewarie, A Hillebrand, M M Schoonheim, et al.
Journal of Neuroimmunology|August 4, 2012
Cerebrospinal fluid anti-whole myelin antibodies are not correlated to magnetic resonance imaging activity in multiple sclerosisP Tewarie, C E Teunissen, C D Dijkstra, et al.
Network Neuroscience (Cambridge, Mass.)|June 23, 2022
Minimum spanning tree analysis of brain networks: A systematic review of network size effects, sensitivity for neuropsychiatric pathology, and disorder specificityN Blomsma, B de Rooy, F Gerritse, et al.
Neuroimage|February 2, 2016
Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactionsP Tewarie, M G Bright, A Hillebrand, et al.
Pageof 2