How reliable are MEG resting-state connectivity metrics?

MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the exte...

Full description

Saved in:
Bibliographic Details
Published inNeuroImage (Orlando, Fla.) Vol. 138; pp. 284 - 293
Main Authors Colclough, G.L., Woolrich, M.W., Tewarie, P.K., Brookes, M.J., Quinn, A.J., Smith, S.M.
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.09.2016
Elsevier Limited
Academic Press
Subjects
Online AccessGet full text
ISSN1053-8119
1095-9572
1095-9572
DOI10.1016/j.neuroimage.2016.05.070

Cover

Loading…
Abstract MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG, localising magnetic sources with a scalar beamformer. We use as empirical criteria that network measures for individual subjects should be repeatable, and that group-level connectivity estimation shows good reproducibility. Using publically-available data from the Human Connectome Project, we test the reliability of 12 network estimation techniques against these criteria. We find that the impact of magnetic field spread or spatial leakage artefact is profound, creates a major confound for many connectivity measures, and can artificially inflate measures of consistency. Among those robust to this effect, we find poor test-retest reliability in phase- or coherence-based metrics such as the phase lag index or the imaginary part of coherency. The most consistent methods for stationary connectivity estimation over all of our tests are simple amplitude envelope correlation and partial correlation measures. •Comparison of the repeatability of 12 common network estimation methods.•Consistency of estimation tested at group-level, subject-level and between subjects.•Best-performing methods are correlations in band-limited power.•Methods should correct for the effects of spatial leakage between sources.
AbstractList MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG, localising magnetic sources with a scalar beamformer. We use as empirical criteria that network measures for individual subjects should be repeatable, and that group-level connectivity estimation shows good reproducibility. Using publically-available data from the Human Connectome Project, we test the reliability of 12 network estimation techniques against these criteria. We find that the impact of magnetic field spread or spatial leakage artefact is profound, creates a major confound for many connectivity measures, and can artificially inflate measures of consistency. Among those robust to this effect, we find poor test-retest reliability in phase- or coherence-based metrics such as the phase lag index or the imaginary part of coherency. The most consistent methods for stationary connectivity estimation over all of our tests are simple amplitude envelope correlation and partial correlation measures.MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG, localising magnetic sources with a scalar beamformer. We use as empirical criteria that network measures for individual subjects should be repeatable, and that group-level connectivity estimation shows good reproducibility. Using publically-available data from the Human Connectome Project, we test the reliability of 12 network estimation techniques against these criteria. We find that the impact of magnetic field spread or spatial leakage artefact is profound, creates a major confound for many connectivity measures, and can artificially inflate measures of consistency. Among those robust to this effect, we find poor test-retest reliability in phase- or coherence-based metrics such as the phase lag index or the imaginary part of coherency. The most consistent methods for stationary connectivity estimation over all of our tests are simple amplitude envelope correlation and partial correlation measures.
MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG, localising magnetic sources with a scalar beamformer. We use as empirical criteria that network measures for individual subjects should be repeatable, and that group-level connectivity estimation shows good reproducibility. Using publically-available data from the Human Connectome Project, we test the reliability of 12 network estimation techniques against these criteria. We find that the impact of magnetic field spread or spatial leakage artefact is profound, creates a major confound for many connectivity measures, and can artificially inflate measures of consistency. Among those robust to this effect, we find poor test-retest reliability in phase- or coherence-based metrics such as the phase lag index or the imaginary part of coherency. The most consistent methods for stationary connectivity estimation over all of our tests are simple amplitude envelope correlation and partial correlation measures.
MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG, localising magnetic sources with a scalar beamformer. We use as empirical criteria that network measures for individual subjects should be repeatable, and that group-level connectivity estimation shows good reproducibility. Using publically-available data from the Human Connectome Project, we test the reliability of 12 network estimation techniques against these criteria. We find that the impact of magnetic field spread or spatial leakage artefact is profound, creates a major confound for many connectivity measures, and can artificially inflate measures of consistency. Among those robust to this effect, we find poor test-retest reliability in phase- or coherence-based metrics such as the phase lag index or the imaginary part of coherency. The most consistent methods for stationary connectivity estimation over all of our tests are simple amplitude envelope correlation and partial correlation measures. • Comparison of the repeatability of 12 common network estimation methods. • Consistency of estimation tested at group-level, subject-level and between subjects. • Best-performing methods are correlations in band-limited power. • Methods should correct for the effects of spatial leakage between sources.
MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network estimation methods for MEG, and little in the way of existing guidance on which ones to employ. In this technical note, we investigate the extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG, localising magnetic sources with a scalar beamformer. We use as empirical criteria that network measures for individual subjects should be repeatable, and that group-level connectivity estimation shows good reproducibility. Using publically-available data from the Human Connectome Project, we test the reliability of 12 network estimation techniques against these criteria. We find that the impact of magnetic field spread or spatial leakage artefact is profound, creates a major confound for many connectivity measures, and can artificially inflate measures of consistency. Among those robust to this effect, we find poor test-retest reliability in phase- or coherence-based metrics such as the phase lag index or the imaginary part of coherency. The most consistent methods for stationary connectivity estimation over all of our tests are simple amplitude envelope correlation and partial correlation measures. •Comparison of the repeatability of 12 common network estimation methods.•Consistency of estimation tested at group-level, subject-level and between subjects.•Best-performing methods are correlations in band-limited power.•Methods should correct for the effects of spatial leakage between sources.
Author Colclough, G.L.
Smith, S.M.
Quinn, A.J.
Woolrich, M.W.
Brookes, M.J.
Tewarie, P.K.
AuthorAffiliation a Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK
c Dept. Engineering Sciences, University of Oxford, Parks Rd, Oxford, UK
b Centre for the Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
d Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
AuthorAffiliation_xml – name: a Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK
– name: d Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
– name: b Centre for the Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
– name: c Dept. Engineering Sciences, University of Oxford, Parks Rd, Oxford, UK
Author_xml – sequence: 1
  givenname: G.L.
  surname: Colclough
  fullname: Colclough, G.L.
  email: giles.colclough@ohba.ox.ac.uk
  organization: Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK
– sequence: 2
  givenname: M.W.
  surname: Woolrich
  fullname: Woolrich, M.W.
  organization: Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK
– sequence: 3
  givenname: P.K.
  surname: Tewarie
  fullname: Tewarie, P.K.
  organization: Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
– sequence: 4
  givenname: M.J.
  surname: Brookes
  fullname: Brookes, M.J.
  organization: Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
– sequence: 5
  givenname: A.J.
  surname: Quinn
  fullname: Quinn, A.J.
  organization: Oxford Centre for Human Brain Activity (OHBA), University of Oxford, Oxford, UK
– sequence: 6
  givenname: S.M.
  surname: Smith
  fullname: Smith, S.M.
  organization: Centre for the Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
BackLink https://www.ncbi.nlm.nih.gov/pubmed/27262239$$D View this record in MEDLINE/PubMed
BookMark eNqNkU1v1DAQhi1URD_gL6BIXLgkjJ04ti98VaVFKuqlnC2vM1m8ZO1iJ1vtv6-jLVvoaQ-WrZnXj2be95Qc-eCRkIJCRYG2H1aVxykGtzZLrFiuVMArEPCCnFBQvFRcsKP5zetSUqqOyWlKKwBQtJGvyDETrGWsVidEXoX7IuLgzGLAwkQsflxc5kIanV-WaTQjFjZ4j3Z0GzduizWO0dn06TV52Zsh4ZvH-4z8_HZxe35VXt9cfj__cl1arsRYGt4b3ilpjOzzoS30C6u6Flrey37R25rJRjTA0fK6bXoGRtVKUsSaNqyz9Rn5uOPeTYs1dhb9GM2g72JePm51ME7_3_Hul16GjebAW8V5Brx_BMTwZ8qL6bVLFofBeAxT0lSCFMAFZ4dIs8m0hpn67pl0FabosxNZJYRQiqsZ-Pbf4fdT__U_C-ROYGNIKWK_l1DQc9R6pZ-i1nPUGrjOUzwZs_9qXY7LhdkGNxwC-LoDYI5v4zDqZB16i52LOW7dBXcI5PMziB2cd9YMv3F7GOIBijbhkA
CitedBy_id crossref_primary_10_1016_j_neuroimage_2024_120992
crossref_primary_10_1007_s10548_021_00828_2
crossref_primary_10_1002_hbm_26644
crossref_primary_10_1016_j_jneumeth_2020_108985
crossref_primary_10_1088_1741_2552_aaaa76
crossref_primary_10_1016_j_neuroimage_2017_09_009
crossref_primary_10_1016_j_clinph_2024_03_036
crossref_primary_10_1016_j_nbd_2023_106047
crossref_primary_10_1016_j_neuroimage_2018_01_056
crossref_primary_10_1162_jocn_a_02011
crossref_primary_10_3389_fnins_2019_00542
crossref_primary_10_1140_epjs_s11734_025_01587_y
crossref_primary_10_3389_fnagi_2021_680200
crossref_primary_10_1016_j_neuroimage_2020_116924
crossref_primary_10_1016_j_clinph_2020_03_031
crossref_primary_10_1098_rsif_2016_0940
crossref_primary_10_1016_j_jneumeth_2019_02_016
crossref_primary_10_1016_j_neuroimage_2021_117864
crossref_primary_10_1016_j_clinph_2021_04_008
crossref_primary_10_1016_j_nicl_2017_03_002
crossref_primary_10_1109_TNSRE_2023_3266024
crossref_primary_10_3389_fnagi_2021_746373
crossref_primary_10_1002_hbm_24578
crossref_primary_10_1371_journal_pcbi_1006160
crossref_primary_10_1016_j_neuroscience_2021_07_012
crossref_primary_10_1016_j_nicl_2018_05_028
crossref_primary_10_1016_j_neuroimage_2022_119752
crossref_primary_10_3389_fnins_2019_00797
crossref_primary_10_1089_brain_2018_0657
crossref_primary_10_1055_a_1195_9190
crossref_primary_10_1016_j_ibneur_2021_05_001
crossref_primary_10_1016_j_ijpsycho_2018_02_008
crossref_primary_10_1016_j_neurobiolaging_2021_11_002
crossref_primary_10_1111_ejn_16292
crossref_primary_10_1162_netn_a_00391
crossref_primary_10_1002_hbm_26747
crossref_primary_10_1109_TBME_2019_2913928
crossref_primary_10_1016_j_neuroimage_2018_08_031
crossref_primary_10_1016_j_clinph_2019_06_006
crossref_primary_10_1016_j_dcn_2022_101119
crossref_primary_10_1016_j_clinph_2017_06_246
crossref_primary_10_1002_hbm_26782
crossref_primary_10_1016_j_bspc_2019_101760
crossref_primary_10_1109_TBME_2022_3154885
crossref_primary_10_1016_j_neuroimage_2016_08_061
crossref_primary_10_1212_WNL_0000000000005333
crossref_primary_10_1007_s11055_019_00850_9
crossref_primary_10_1089_brain_2019_0723
crossref_primary_10_1016_j_neuroimage_2018_03_004
crossref_primary_10_3389_fpsyt_2020_551952
crossref_primary_10_3389_fnhum_2021_649074
crossref_primary_10_1162_netn_a_00267
crossref_primary_10_3389_fnetp_2023_1338864
crossref_primary_10_1038_s41598_022_20274_9
crossref_primary_10_3390_brainsci9060144
crossref_primary_10_7554_eLife_36011
crossref_primary_10_1016_j_nicl_2020_102251
crossref_primary_10_1016_j_neuroimage_2021_118253
crossref_primary_10_1016_j_neuroimage_2023_120006
crossref_primary_10_1016_j_jad_2020_12_081
crossref_primary_10_1002_hbm_25683
crossref_primary_10_1186_s13195_024_01576_8
crossref_primary_10_1088_2632_072X_ac3ddd
crossref_primary_10_1016_j_neucli_2024_102954
crossref_primary_10_1016_j_brs_2023_10_008
crossref_primary_10_1016_j_nicl_2025_103754
crossref_primary_10_1162_netn_a_00135
crossref_primary_10_1162_imag_a_00195
crossref_primary_10_1016_j_neuroimage_2020_117674
crossref_primary_10_1038_s41598_021_95363_2
crossref_primary_10_1038_s41598_017_07846_w
crossref_primary_10_3389_fnins_2022_944391
crossref_primary_10_1016_j_neuroimage_2020_117551
crossref_primary_10_3390_medsci9020020
crossref_primary_10_1016_j_neuroimage_2019_116386
crossref_primary_10_1016_j_neuroimage_2017_10_003
crossref_primary_10_1016_j_neuroimage_2024_120834
crossref_primary_10_1016_j_neuroimage_2019_116313
crossref_primary_10_1093_cercor_bhad221
crossref_primary_10_1111_pcn_13362
crossref_primary_10_1002_jnr_24896
crossref_primary_10_3389_fnhum_2021_732946
crossref_primary_10_1016_j_euroneuro_2021_04_005
crossref_primary_10_1038_s41598_025_90166_1
crossref_primary_10_1016_j_nicl_2023_103317
crossref_primary_10_3389_fpsyt_2017_00041
crossref_primary_10_1038_s42003_022_03727_9
crossref_primary_10_1016_j_cortex_2017_09_021
crossref_primary_10_1016_j_eplepsyres_2018_06_001
crossref_primary_10_1016_j_neuroimage_2021_117822
crossref_primary_10_1051_epjnbp_2017001
crossref_primary_10_3389_fnins_2022_782474
crossref_primary_10_1016_j_nicl_2022_103040
crossref_primary_10_1038_s41598_025_86008_9
crossref_primary_10_1016_j_neuroimage_2023_120424
crossref_primary_10_3389_fphys_2020_598694
crossref_primary_10_62051_x2r7tm73
crossref_primary_10_1016_j_neuroimage_2022_119041
crossref_primary_10_1093_cercor_bhac023
crossref_primary_10_1038_s41398_021_01467_8
crossref_primary_10_7554_eLife_91044_3
crossref_primary_10_1016_j_bpsc_2019_08_012
crossref_primary_10_1016_j_neuroimage_2017_11_016
crossref_primary_10_1016_j_neuroimage_2021_117829
crossref_primary_10_1523_ENEURO_0101_20_2020
crossref_primary_10_1038_s41551_020_00614_8
crossref_primary_10_1016_j_nicl_2022_103036
crossref_primary_10_1038_s42003_024_06807_0
crossref_primary_10_1162_imag_a_00296
crossref_primary_10_1016_j_neurobiolaging_2021_10_016
crossref_primary_10_1016_j_neuroimage_2022_118891
crossref_primary_10_1016_j_irbm_2017_03_002
crossref_primary_10_1016_j_jneumeth_2020_108688
crossref_primary_10_1016_j_neuroimage_2023_120212
crossref_primary_10_1016_j_neuroimage_2023_120332
crossref_primary_10_1186_s13195_022_00970_4
crossref_primary_10_1111_psyp_14268
crossref_primary_10_1016_j_neuroimage_2018_10_079
crossref_primary_10_1162_imag_a_00020
crossref_primary_10_1016_j_nicl_2023_103465
crossref_primary_10_1016_j_clinph_2017_12_003
crossref_primary_10_1016_j_clinph_2020_12_021
crossref_primary_10_1016_j_neuroimage_2021_118457
crossref_primary_10_1038_s42003_022_03974_w
crossref_primary_10_1016_j_neuroimage_2017_02_076
crossref_primary_10_1016_j_neuroimage_2021_118331
crossref_primary_10_1093_brain_awx217
crossref_primary_10_1007_s10548_020_00757_6
crossref_primary_10_1111_epi_17898
crossref_primary_10_1016_j_neuroimage_2022_119175
crossref_primary_10_1088_1741_2552_ab8113
crossref_primary_10_1016_j_clinph_2019_09_014
crossref_primary_10_1016_j_neuroimage_2022_119177
crossref_primary_10_1002_hbm_26610
crossref_primary_10_1371_journal_pbio_3002314
crossref_primary_10_1109_TBME_2021_3049199
crossref_primary_10_1152_jn_00860_2017
crossref_primary_10_1371_journal_pcbi_1006007
crossref_primary_10_1093_scan_nsae026
crossref_primary_10_1007_s10548_023_00965_w
crossref_primary_10_1016_j_neuroimage_2020_116537
crossref_primary_10_1016_j_neuroimage_2020_116538
crossref_primary_10_1016_j_neuroimage_2018_04_077
crossref_primary_10_1097_j_pain_0000000000002255
crossref_primary_10_3390_brainsci11121590
crossref_primary_10_1016_j_pscychresns_2023_111767
crossref_primary_10_1016_j_jad_2023_08_096
crossref_primary_10_1523_JNEUROSCI_2155_20_2020
crossref_primary_10_1038_s41598_025_86192_8
crossref_primary_10_1038_s41562_019_0717_0
crossref_primary_10_1212_WNL_0000000000200386
crossref_primary_10_1016_j_neuroimage_2023_120218
crossref_primary_10_1016_j_jad_2019_06_066
crossref_primary_10_1016_j_heliyon_2024_e36463
crossref_primary_10_3389_fneur_2022_814940
crossref_primary_10_1016_j_neuroimage_2019_116513
crossref_primary_10_1080_10749357_2020_1864986
crossref_primary_10_1002_hbm_24981
crossref_primary_10_1016_j_neuroimage_2016_11_064
crossref_primary_10_1016_j_neuroimage_2022_119006
crossref_primary_10_1038_s41467_018_05316_z
crossref_primary_10_1016_j_clinph_2018_03_019
crossref_primary_10_1088_1741_2552_aacfe4
crossref_primary_10_1089_neu_2023_0315
crossref_primary_10_1186_s10194_023_01695_x
crossref_primary_10_1016_j_neuroimage_2018_02_018
crossref_primary_10_1016_j_neurobiolaging_2020_03_009
crossref_primary_10_1016_j_tins_2022_03_011
crossref_primary_10_1109_TMI_2017_2739740
crossref_primary_10_1016_j_eplepsyres_2020_106324
crossref_primary_10_1093_braincomms_fcae348
crossref_primary_10_1089_brain_2019_0662
crossref_primary_10_1016_j_neurobiolaging_2020_07_029
crossref_primary_10_1016_j_intell_2022_101665
crossref_primary_10_1016_j_neuroimage_2025_121056
crossref_primary_10_1016_j_clinph_2017_05_012
crossref_primary_10_1152_jn_00333_2020
crossref_primary_10_3390_brainsci10090644
crossref_primary_10_7554_eLife_91044
crossref_primary_10_7717_peerj_10057
crossref_primary_10_1038_s41598_019_45289_7
crossref_primary_10_1016_j_drugalcdep_2020_108401
crossref_primary_10_3389_fnins_2018_00603
crossref_primary_10_1016_j_neuroimage_2017_04_038
crossref_primary_10_1007_s10548_021_00859_9
crossref_primary_10_1088_1741_2552_ab234b
crossref_primary_10_1093_cercor_bhaa286
crossref_primary_10_1016_j_neuroimage_2020_117051
crossref_primary_10_3389_fninf_2017_00043
crossref_primary_10_3390_chemosensors7030045
crossref_primary_10_1162_netn_a_00305
crossref_primary_10_1111_ejn_15936
crossref_primary_10_1111_psyp_13934
crossref_primary_10_31083_j_jin2301019
crossref_primary_10_3233_JAD_230825
crossref_primary_10_1016_j_schres_2019_10_023
crossref_primary_10_1162_netn_a_00303
crossref_primary_10_1111_ejn_14960
crossref_primary_10_3389_fnins_2018_00506
crossref_primary_10_3390_diagnostics12010084
crossref_primary_10_1038_s41598_023_35808_y
crossref_primary_10_1298_ptr_R0035
crossref_primary_10_1002_alz_12311
crossref_primary_10_1016_j_neuroimage_2020_116998
crossref_primary_10_1016_j_bspc_2024_105977
crossref_primary_10_3390_jpm10020034
crossref_primary_10_52586_5047
crossref_primary_10_1002_hbm_25726
crossref_primary_10_1002_hbm_25967
crossref_primary_10_1016_j_clinph_2025_02_264
crossref_primary_10_1109_TNSRE_2022_3208374
crossref_primary_10_1002_brb3_2097
crossref_primary_10_1089_neu_2020_7450
crossref_primary_10_1002_acn3_50966
crossref_primary_10_1093_braincomms_fcaa094
crossref_primary_10_1162_imag_a_00119
crossref_primary_10_11154_pain_36_42
crossref_primary_10_1038_s41398_020_00998_w
crossref_primary_10_3389_fnins_2023_1176825
crossref_primary_10_1093_brain_awy180
crossref_primary_10_1109_JBHI_2020_3008731
crossref_primary_10_1016_j_neuroimage_2017_07_038
crossref_primary_10_1016_j_neuroimage_2018_10_020
crossref_primary_10_3389_fnhum_2021_717810
crossref_primary_10_1002_hbm_25835
crossref_primary_10_1016_j_neuroimage_2022_119681
crossref_primary_10_1016_j_jns_2021_120121
crossref_primary_10_1371_journal_pbio_3001735
crossref_primary_10_1007_s00406_021_01371_8
crossref_primary_10_7554_eLife_20178
crossref_primary_10_1016_j_neurobiolaging_2021_01_035
crossref_primary_10_7554_eLife_60988
crossref_primary_10_1162_netn_a_00245
crossref_primary_10_1007_s10548_020_00753_w
crossref_primary_10_1016_j_neuroimage_2021_118516
crossref_primary_10_1002_hbm_24937
crossref_primary_10_1162_netn_a_00361
crossref_primary_10_1162_netn_a_00362
crossref_primary_10_1186_s10194_020_01200_8
crossref_primary_10_1016_j_neuroimage_2019_04_061
crossref_primary_10_1038_s41598_022_07730_2
crossref_primary_10_1080_10749357_2019_1658429
crossref_primary_10_1371_journal_pone_0319213
crossref_primary_10_3390_diagnostics11071234
crossref_primary_10_1177_09544119221092503
crossref_primary_10_3390_e23050500
crossref_primary_10_1162_netn_a_00114
crossref_primary_10_1016_j_clinph_2023_03_002
crossref_primary_10_1088_1741_2552_ab9064
crossref_primary_10_1152_jn_00530_2020
crossref_primary_10_3389_fnins_2020_00648
crossref_primary_10_1088_1741_2552_ad6187
crossref_primary_10_1162_netn_a_00353
crossref_primary_10_7717_peerj_17721
crossref_primary_10_1038_s42003_023_04648_x
crossref_primary_10_1016_j_neuroimage_2021_118407
crossref_primary_10_1038_s41598_021_86215_0
crossref_primary_10_1002_hbm_25578
crossref_primary_10_1016_j_neuroimage_2019_116288
crossref_primary_10_1016_j_neuroimage_2021_118171
crossref_primary_10_3389_fpsyt_2023_1250268
crossref_primary_10_1016_j_neuroimage_2022_119344
crossref_primary_10_1002_hbm_26466
crossref_primary_10_1088_2057_1976_aa9c64
crossref_primary_10_1162_netn_a_00103
crossref_primary_10_1162_netn_a_00224
crossref_primary_10_1162_netn_a_00226
crossref_primary_10_1038_s41386_023_01628_x
crossref_primary_10_1016_j_neuron_2020_12_007
crossref_primary_10_1038_s41598_017_10235_y
crossref_primary_10_1002_hbm_70018
crossref_primary_10_1063_5_0018826
crossref_primary_10_1016_j_jneumeth_2021_109424
crossref_primary_10_1016_j_neuroimage_2020_117001
crossref_primary_10_1371_journal_pcbi_1009252
crossref_primary_10_1016_j_clinph_2023_10_014
crossref_primary_10_1088_1361_6420_ab8713
crossref_primary_10_1002_advs_202306321
crossref_primary_10_1152_jn_00293_2018
crossref_primary_10_1089_brain_2021_0059
crossref_primary_10_1016_j_neuroimage_2023_120186
crossref_primary_10_1002_hbm_25247
crossref_primary_10_1016_j_neuroimage_2019_06_006
crossref_primary_10_1162_netn_a_00331
crossref_primary_10_1007_s10548_023_00968_7
crossref_primary_10_1016_j_pneurobio_2021_102076
crossref_primary_10_3389_fnsyn_2020_00007
crossref_primary_10_1093_braincomms_fcad168
crossref_primary_10_3389_fnins_2024_1237245
crossref_primary_10_1186_s13229_023_00570_5
crossref_primary_10_1002_hbm_24820
crossref_primary_10_1109_TMI_2018_2873423
crossref_primary_10_1186_s13195_020_00632_3
crossref_primary_10_3390_e20050311
Cites_doi 10.1109/TPAMI.2005.159
10.2307/1912791
10.1089/brain.2014.0280
10.1016/j.neuroimage.2005.12.057
10.1371/journal.pone.0044633
10.1016/j.neuroimage.2013.02.008
10.1038/nn.3101
10.1016/S0375-9601(97)00635-X
10.1016/j.clinph.2012.01.011
10.1016/j.tics.2013.09.016
10.3389/fnins.2014.00405
10.1016/j.euroneuro.2012.10.010
10.1109/10.623056
10.1016/j.neuroimage.2012.03.048
10.1016/j.neuroimage.2009.05.035
10.1016/j.neuroimage.2014.04.038
10.1073/pnas.0913863107
10.1152/jn.00335.2011
10.1016/j.clinph.2004.04.029
10.1016/j.neuroimage.2012.04.046
10.1016/j.neuroimage.2010.08.063
10.1002/hbm.20263
10.3389/fncom.2014.00061
10.1002/hbm.20745
10.1371/journal.pone.0108648
10.1016/j.neuroimage.2009.10.078
10.1002/hbm.20080
10.1371/journal.pcbi.1003548
10.1098/rsta.2011.0616
10.1214/12-EJS740
10.1038/nrn3801
10.1016/j.neuroimage.2011.01.055
10.1016/j.neuroimage.2015.03.071
10.7554/eLife.01867
10.1016/j.neuroimage.2013.05.041
10.1016/j.neuroimage.2014.03.065
10.1038/385157a0
10.1016/j.neuroimage.2015.04.030
10.1142/S0218127400001560
10.1103/PhysRevLett.100.234101
10.1016/j.tics.2012.02.004
10.1093/brain/awn262
10.1016/j.neuroimage.2013.05.056
10.1002/hbm.22943
10.1002/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C
10.1007/PL00007990
10.1016/j.neuroimage.2011.04.041
10.1109/TBME.2006.873752
10.1016/j.neuroimage.2006.11.012
10.1016/j.neuroimage.2013.12.066
10.1073/pnas.1112685108
10.1002/hbm.22279
10.1016/j.neuroimage.2013.04.062
10.1002/hbm.20346
10.1109/TBME.2013.2286394
ContentType Journal Article
Copyright 2016 The Authors
Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
Copyright Elsevier Limited Sep 1, 2016
2016 The Authors 2016
Copyright_xml – notice: 2016 The Authors
– notice: Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
– notice: Copyright Elsevier Limited Sep 1, 2016
– notice: 2016 The Authors 2016
DBID 6I.
AAFTH
AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
3V.
7TK
7X7
7XB
88E
88G
8AO
8FD
8FE
8FH
8FI
8FJ
8FK
ABUWG
AFKRA
AZQEC
BBNVY
BENPR
BHPHI
CCPQU
DWQXO
FR3
FYUFA
GHDGH
GNUQQ
HCIFZ
K9.
LK8
M0S
M1P
M2M
M7P
P64
PHGZM
PHGZT
PJZUB
PKEHL
PPXIY
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
PSYQQ
Q9U
RC3
7X8
7QO
5PM
DOI 10.1016/j.neuroimage.2016.05.070
DatabaseName ScienceDirect Open Access Titles
Elsevier:ScienceDirect:Open Access
CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
ProQuest Central (Corporate)
Neurosciences Abstracts
Health & Medical Collection
ProQuest Central (purchase pre-March 2016)
Medical Database (Alumni Edition)
Psychology Database (Alumni)
ProQuest Pharma Collection
Technology Research Database
ProQuest SciTech Collection
ProQuest Natural Science Collection
Hospital Premium Collection
Hospital Premium Collection (Alumni Edition)
ProQuest Central (Alumni) (purchase pre-March 2016)
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
ProQuest Central Essentials
Biological Science Collection
ProQuest
Natural Science Collection
ProQuest One
ProQuest Central
Engineering Research Database
Health Research Premium Collection
Health Research Premium Collection (Alumni)
ProQuest Central Student
SciTech Premium Collection
ProQuest Health & Medical Complete (Alumni)
Biological Sciences
ProQuest Health & Medical Collection
Medical Database
Psychology Database
Biological Science Database
Biotechnology and BioEngineering Abstracts
ProQuest Central Premium
ProQuest One Academic (New)
ProQuest Health & Medical Research Collection
ProQuest One Academic Middle East (New)
ProQuest One Health & Nursing
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic
ProQuest One Academic UKI Edition
ProQuest Central China
ProQuest One Psychology
ProQuest Central Basic
Genetics Abstracts
MEDLINE - Academic
Biotechnology Research Abstracts
PubMed Central (Full Participant titles)
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
ProQuest One Psychology
ProQuest Central Student
Technology Research Database
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
ProQuest Health & Medical Complete (Alumni)
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest One Health & Nursing
ProQuest Natural Science Collection
ProQuest Pharma Collection
ProQuest Central China
ProQuest Central
ProQuest One Applied & Life Sciences
ProQuest Health & Medical Research Collection
Genetics Abstracts
Health Research Premium Collection
Health and Medicine Complete (Alumni Edition)
Natural Science Collection
ProQuest Central Korea
Health & Medical Research Collection
Biological Science Collection
ProQuest Central (New)
ProQuest Medical Library (Alumni)
ProQuest Biological Science Collection
ProQuest Central Basic
ProQuest One Academic Eastern Edition
ProQuest Hospital Collection
Health Research Premium Collection (Alumni)
ProQuest Psychology Journals (Alumni)
Biological Science Database
ProQuest SciTech Collection
Neurosciences Abstracts
ProQuest Hospital Collection (Alumni)
Biotechnology and BioEngineering Abstracts
ProQuest Health & Medical Complete
ProQuest Medical Library
ProQuest Psychology Journals
ProQuest One Academic UKI Edition
Engineering Research Database
ProQuest One Academic
ProQuest One Academic (New)
ProQuest Central (Alumni)
MEDLINE - Academic
Biotechnology Research Abstracts
DatabaseTitleList MEDLINE - Academic
Engineering Research Database


MEDLINE

ProQuest One Psychology
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: EIF
  name: MEDLINE
  url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search
  sourceTypes: Index Database
– sequence: 3
  dbid: BENPR
  name: ProQuest Central
  url: https://www.proquest.com/central
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Medicine
EISSN 1095-9572
EndPage 293
ExternalDocumentID PMC5056955
4320860601
27262239
10_1016_j_neuroimage_2016_05_070
S1053811916301914
Genre Evaluation Study
Journal Article
Comparative Study
GrantInformation_xml – fundername: Wellcome Trust
  grantid: 092753
– fundername: NIMH NIH HHS
  grantid: U54 MH091657
– fundername: Medical Research Council
  grantid: MR/M006301/1
– fundername: Medical Research Council
  grantid: MR/K005464/1
GroupedDBID ---
--K
--M
.1-
.FO
.~1
0R~
123
1B1
1RT
1~.
1~5
29N
4.4
457
4G.
53G
5RE
5VS
7-5
71M
7X7
88E
8AO
8FE
8FH
8FI
8FJ
8P~
9JM
AABNK
AAEDT
AAEDW
AAFWJ
AAIKJ
AAKOC
AALRI
AAOAW
AAQFI
AAQXK
AATTM
AAXKI
AAXLA
AAXUO
AAYWO
ABBQC
ABCQJ
ABFNM
ABFRF
ABIVO
ABJNI
ABMAC
ABMZM
ABUWG
ABXDB
ACDAQ
ACGFO
ACGFS
ACIEU
ACPRK
ACRLP
ACRPL
ACVFH
ADBBV
ADCNI
ADEZE
ADFGL
ADFRT
ADMUD
ADNMO
ADVLN
ADXHL
AEBSH
AEFWE
AEIPS
AEKER
AENEX
AEUPX
AFJKZ
AFKRA
AFPKN
AFPUW
AFRHN
AFTJW
AFXIZ
AGCQF
AGHFR
AGQPQ
AGUBO
AGWIK
AGYEJ
AHHHB
AHMBA
AIEXJ
AIGII
AIIUN
AIKHN
AITUG
AJRQY
AJUYK
AKBMS
AKRLJ
AKRWK
AKYEP
ALMA_UNASSIGNED_HOLDINGS
AMRAJ
ANKPU
ANZVX
APXCP
ASPBG
AVWKF
AXJTR
AZFZN
AZQEC
BBNVY
BENPR
BHPHI
BKOJK
BLXMC
BNPGV
BPHCQ
BVXVI
CAG
CCPQU
COF
CS3
DM4
DU5
DWQXO
EBS
EFBJH
EFKBS
EJD
EO8
EO9
EP2
EP3
F5P
FDB
FEDTE
FGOYB
FIRID
FNPLU
FYGXN
FYUFA
G-2
G-Q
GBLVA
GNUQQ
GROUPED_DOAJ
HCIFZ
HDW
HEI
HMCUK
HMK
HMO
HMQ
HVGLF
HZ~
IHE
J1W
KOM
LG5
LK8
LX8
M1P
M29
M2M
M2V
M41
M7P
MO0
MOBAO
N9A
O-L
O9-
OAUVE
OK1
OVD
OZT
P-8
P-9
P2P
PC.
PHGZM
PHGZT
PJZUB
PPXIY
PQGLB
PQQKQ
PROAC
PSQYO
PSYQQ
PUEGO
Q38
R2-
ROL
RPZ
SAE
SCC
SDF
SDG
SDP
SES
SEW
SNS
SSH
SSN
SSZ
T5K
TEORI
UKHRP
UV1
WUQ
XPP
YK3
Z5R
ZMT
ZU3
~G-
3V.
6I.
AACTN
AADPK
AAFTH
AAIAV
ABLVK
ABYKQ
AFKWA
AJBFU
AJOXV
AMFUW
C45
EFLBG
LCYCR
NCXOZ
RIG
ZA5
AAYXX
AGRNS
ALIPV
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
7TK
7XB
8FD
8FK
FR3
K9.
P64
PKEHL
PQEST
PQUKI
PRINS
Q9U
RC3
7X8
7QO
5PM
ID FETCH-LOGICAL-c597t-a5fa5d98aa8faa8160fbc9d6065f8fbfc32847405ec5364f20a93981ee3142dc3
IEDL.DBID .~1
ISSN 1053-8119
1095-9572
IngestDate Thu Aug 21 14:03:03 EDT 2025
Thu Jul 10 17:40:32 EDT 2025
Fri Jul 11 05:04:41 EDT 2025
Wed Aug 13 02:27:39 EDT 2025
Mon Jul 21 06:03:58 EDT 2025
Tue Jul 01 03:01:48 EDT 2025
Thu Apr 24 22:57:26 EDT 2025
Fri Feb 23 02:25:07 EST 2024
Tue Aug 26 20:08:40 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Keywords Functional connectivity
Source leakage
Connectome
Magnetic field spread
Network analysis
MEG
Language English
License This is an open access article under the CC BY license.
Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c597t-a5fa5d98aa8faa8160fbc9d6065f8fbfc32847405ec5364f20a93981ee3142dc3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ObjectType-Undefined-3
OpenAccessLink https://www.sciencedirect.com/science/article/pii/S1053811916301914
PMID 27262239
PQID 1877799592
PQPubID 2031077
PageCount 10
ParticipantIDs pubmedcentral_primary_oai_pubmedcentral_nih_gov_5056955
proquest_miscellaneous_1808705752
proquest_miscellaneous_1800701305
proquest_journals_1877799592
pubmed_primary_27262239
crossref_primary_10_1016_j_neuroimage_2016_05_070
crossref_citationtrail_10_1016_j_neuroimage_2016_05_070
elsevier_sciencedirect_doi_10_1016_j_neuroimage_2016_05_070
elsevier_clinicalkey_doi_10_1016_j_neuroimage_2016_05_070
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate September 2016
2016-09-00
2016-Sep
20160901
PublicationDateYYYYMMDD 2016-09-01
PublicationDate_xml – month: 09
  year: 2016
  text: September 2016
PublicationDecade 2010
PublicationPlace United States
PublicationPlace_xml – name: United States
– name: Amsterdam
PublicationTitle NeuroImage (Orlando, Fla.)
PublicationTitleAlternate Neuroimage
PublicationYear 2016
Publisher Elsevier Inc
Elsevier Limited
Academic Press
Publisher_xml – name: Elsevier Inc
– name: Elsevier Limited
– name: Academic Press
References Hipp, Hawellek, Corbetta, Siegel, Engel (bb0090) 2012; 15
Brookes, Woolrich, Barnes (bb0035) 2012; 63
Van Veen, van Drongelen, Yuchtman, Suzuki (bb0260) 1997; 44
Larson-Prior, Oostenveld, Della Penna, Michalareas, Prior, Babajani-Feremi, Schoffelen, Marzetti, de Pasquale, Di Pompeo, Stout, Woolrich, Luo, Bucholz, Fries, Pizella, Romani, Corbetta, Snyder, Consortium, W.M.H. (bb0120) 2013; 80
Stam, de Haan, Daffertshofer, Jones, Manshanden, van Cappellen van Walsum, Montez, Verbunt, de Munck, van Dijk, Berendse, Sheltens (bb0240) 2009; 132
Vinck, Oostenveld, van Wingerden, Battaglia, Pennartz (bb0265) 2011; 55
Palva, Palva (bb0185) 2012; 16
Lachaux, Rodriguez, le van Quyen, Lutz, Martinerie, Varela (bb0115) 2000; 10
Colclough, Brookes, Smith, Woolrich (bb0045) 2015; 117
Van Essen, Smith, Barch, Behrens, Yacoub, Ugurbil, for the WU-Minn HCP Consortium (bb0250) 2013; 80
Granger (bb0075) 1969; 37
Hardmeier, Hatz, Bousleiman, Schindler, Stam, Fuhr (bb0080) 2014; 9
Woolrich, Hunt, Groves, Barnes (bb0285) 2011; 57
Jin, Seol, Kim, Chung (bb0100) 2011; 106
Baccala, Sameshima (bb0015) 2001; 84
Wilmer, de Lussanet, Lappe (bb0280) 2012; 7
Leistritz, Pester, Doering, Schiecke, Babiloni, Astolfi, Witte (bb0125) 2013; 371
van Straaten, Stam (bb0255) 2013; 23
Luckhoo, Hale, Stokes, Nobre, Morris, Brookes, Woolrich (bb0130) 2012; 62
Astolfi, Cincotti, Mattia, Marciani, Salinari, de Vico Fallani, Baccala, Ursino, Zavaglia, Ding, Edgar, Miller, He, Babiloni (bb0005) 2007; 28
de Pasquale, Della Penna, Snyder, Lewis, Mantini, Marzetti, Belardinelli, Ciancetta, Pizzella, Romani, Corbetta (bb0055) 2010; 107
Tewarie, Hillebrand, van Dellen, Schoonheim, Barkhof, Polman, Beauliue, Gong, van Dijk, Stam (bb0245) 2014; 97
Wang, Benar, Quilichini, Friston, Jirsa, Bernand (bb0270) 2014; 8
Aydore, Pantazis, Leahy (bb0010) 2013; 74
Stam, Nolte, Daffertshofer (bb0235) 2007; 28
Stam, van Straaten (bb0230) 2012; 123
Mehrkanoon, Breakspear, Britz, Boonstra (bb0155) 2014; 4
O'Neill, Bauer, Woolrich, Morris, Barnes, Brookes (bb0170) 2015; 115
Roelfsema, Engel, König, Singer (bb0200) 1997; 385
Smith, Vidaurre, Beckmann, Glasser, Jenkinson, Miller, Nichols, Robinson, Salimi-Khorshidi, Woolrich, Barch, Ugurbil, Van Essen (bb0220) 2013; 17
Nolte, Bai, Wheaton, Mari, Vorbach, Hallett (bb0160) 2004; 115
Hauk, Stenroos (bb0085) 2014; 35
Brookes, Woolrich, Luckhoo, Price, Hale, Stephenson, Barnes, Smith, Morris (bb0030) 2011; 108
Marrelec, Krainik, Duffau, Pelegrini-Issac, Lehericy, Doyon, Benali (bb0140) 2006; 32
Nolte, Ziehe, Nikulin, Schlögl, Krämer, Brismar, Müller (bb0165) 2008; 100
Lachaux, Rodriguez, Martinerie, Varela (bb0110) 1999; 8
Baker, Brookes, Rezek, Smith, Behrens, Smith, Woolrich (bb0020) 2014; 3
Robinson, Vrba (bb0195) 1999
Deuker, Bullmore, Smith, Christensen, Nathan, Rockstroh, Bassett (bb0065) 2009; 47
Dalal, Sekihara, Nagarajan (bb0050) 2006; 53
de Pasquale, Della Penna, Sporns, Romani, Corbetta (bb0060) 2015
Wens, Marty, Mary, Op de Beeck, Goldman, Van Bogaert, Peigneux, De Tiege (bb0275) 2015; 36
Brookes, Stevenson, Barnes, Hillebrand, Simpson, Francis, Morris (bb0025) 2007; 34
Paluš (bb0180) 1997; 235
Omidvarnia, Azemi, Boashash, O’Toole, Colditz, Vanhatalo (bb0175) 2014; 61
Smith, Beckmann, Ramnani, Woolrich, Bannister, Jenkinson, Matthews, McGonigle (bb0210) 2005; 24
Schoffelen, Gross (bb0205) 2009; 30
Gollo, Mirasso, Sporns, Breakspear (bb0070) 2014; 10
Stam (bb0225) 2014; 15
Smith, Miller, Salimi-Khorshidi, Webster, Beckmann, Nichols, Ramsey, Woolrich (bb0215) 2011; 54
Brookes, O'Neill, Hall, Woolrich, Baker, Palazzo-Corner, Robson, Barnes (bb0040) 2014; 91
Mazumder, Hastie (bb0150) 2012; 6
Kaminski, Blinowska (bb0105) 2014; 8
Maldjian, Davenport, Whitlow (bb0135) 2014; 96
Hui, Pantazis, Bressler, Leahy (bb0095) 2010; 49
Marzetti, Della Penna, Snyder, Pizzella, Nolte, de Pasquale, Romani, Corbetta (bb0145) 2013; 79
Peng, Long, Ding (bb0190) 2005; 27
Stam (10.1016/j.neuroimage.2016.05.070_bb0235) 2007; 28
Nolte (10.1016/j.neuroimage.2016.05.070_bb0165) 2008; 100
Paluš (10.1016/j.neuroimage.2016.05.070_bb0180) 1997; 235
Brookes (10.1016/j.neuroimage.2016.05.070_bb0025) 2007; 34
Wang (10.1016/j.neuroimage.2016.05.070_bb0270) 2014; 8
Dalal (10.1016/j.neuroimage.2016.05.070_bb0050) 2006; 53
Nolte (10.1016/j.neuroimage.2016.05.070_bb0160) 2004; 115
Aydore (10.1016/j.neuroimage.2016.05.070_bb0010) 2013; 74
Robinson (10.1016/j.neuroimage.2016.05.070_bb0195) 1999
de Pasquale (10.1016/j.neuroimage.2016.05.070_bb0060) 2015
Luckhoo (10.1016/j.neuroimage.2016.05.070_bb0130) 2012; 62
Smith (10.1016/j.neuroimage.2016.05.070_bb0210) 2005; 24
Gollo (10.1016/j.neuroimage.2016.05.070_bb0070) 2014; 10
Vinck (10.1016/j.neuroimage.2016.05.070_bb0265) 2011; 55
Smith (10.1016/j.neuroimage.2016.05.070_bb0215) 2011; 54
de Pasquale (10.1016/j.neuroimage.2016.05.070_bb0055) 2010; 107
Maldjian (10.1016/j.neuroimage.2016.05.070_bb0135) 2014; 96
Palva (10.1016/j.neuroimage.2016.05.070_bb0185) 2012; 16
Hauk (10.1016/j.neuroimage.2016.05.070_bb0085) 2014; 35
Peng (10.1016/j.neuroimage.2016.05.070_bb0190) 2005; 27
Brookes (10.1016/j.neuroimage.2016.05.070_bb0040) 2014; 91
Jin (10.1016/j.neuroimage.2016.05.070_bb0100) 2011; 106
Smith (10.1016/j.neuroimage.2016.05.070_bb0220) 2013; 17
Lachaux (10.1016/j.neuroimage.2016.05.070_bb0115) 2000; 10
Roelfsema (10.1016/j.neuroimage.2016.05.070_bb0200) 1997; 385
Mazumder (10.1016/j.neuroimage.2016.05.070_bb0150) 2012; 6
Van Essen (10.1016/j.neuroimage.2016.05.070_bb0250) 2013; 80
Woolrich (10.1016/j.neuroimage.2016.05.070_bb0285) 2011; 57
Tewarie (10.1016/j.neuroimage.2016.05.070_bb0245) 2014; 97
Hardmeier (10.1016/j.neuroimage.2016.05.070_bb0080) 2014; 9
Stam (10.1016/j.neuroimage.2016.05.070_bb0225) 2014; 15
Brookes (10.1016/j.neuroimage.2016.05.070_bb0030) 2011; 108
Colclough (10.1016/j.neuroimage.2016.05.070_bb0045) 2015; 117
Larson-Prior (10.1016/j.neuroimage.2016.05.070_bb0120) 2013; 80
Omidvarnia (10.1016/j.neuroimage.2016.05.070_bb0175) 2014; 61
Baccala (10.1016/j.neuroimage.2016.05.070_bb0015) 2001; 84
Marrelec (10.1016/j.neuroimage.2016.05.070_bb0140) 2006; 32
Granger (10.1016/j.neuroimage.2016.05.070_bb0075) 1969; 37
Stam (10.1016/j.neuroimage.2016.05.070_bb0230) 2012; 123
Brookes (10.1016/j.neuroimage.2016.05.070_bb0035) 2012; 63
Baker (10.1016/j.neuroimage.2016.05.070_bb0020) 2014; 3
Leistritz (10.1016/j.neuroimage.2016.05.070_bb0125) 2013; 371
O'Neill (10.1016/j.neuroimage.2016.05.070_bb0170) 2015; 115
Wilmer (10.1016/j.neuroimage.2016.05.070_bb0280) 2012; 7
Marzetti (10.1016/j.neuroimage.2016.05.070_bb0145) 2013; 79
Mehrkanoon (10.1016/j.neuroimage.2016.05.070_bb0155) 2014; 4
Van Veen (10.1016/j.neuroimage.2016.05.070_bb0260) 1997; 44
van Straaten (10.1016/j.neuroimage.2016.05.070_bb0255) 2013; 23
Schoffelen (10.1016/j.neuroimage.2016.05.070_bb0205) 2009; 30
Hui (10.1016/j.neuroimage.2016.05.070_bb0095) 2010; 49
Hipp (10.1016/j.neuroimage.2016.05.070_bb0090) 2012; 15
Stam (10.1016/j.neuroimage.2016.05.070_bb0240) 2009; 132
Deuker (10.1016/j.neuroimage.2016.05.070_bb0065) 2009; 47
Astolfi (10.1016/j.neuroimage.2016.05.070_bb0005) 2007; 28
Wens (10.1016/j.neuroimage.2016.05.070_bb0275) 2015; 36
Kaminski (10.1016/j.neuroimage.2016.05.070_bb0105) 2014; 8
Lachaux (10.1016/j.neuroimage.2016.05.070_bb0110) 1999; 8
References_xml – volume: 24
  start-page: 248
  year: 2005
  end-page: 257
  ident: bb0210
  article-title: Variability in fMRI: a re-examination of inter-session differences
  publication-title: Hum. Brain Mapp.
– volume: 235
  start-page: 341
  year: 1997
  end-page: 351
  ident: bb0180
  article-title: Detecting phase synchronization in noisy systems
  publication-title: Phys. Lett. A
– volume: 132
  start-page: 213
  year: 2009
  end-page: 224
  ident: bb0240
  article-title: Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease
  publication-title: Brain
– volume: 30
  start-page: 1857
  year: 2009
  end-page: 1865
  ident: bb0205
  article-title: Source connectivity analysis with MEG and EEG
  publication-title: Hum. Brain Mapp.
– volume: 117
  start-page: 439
  year: 2015
  end-page: 448
  ident: bb0045
  article-title: A symmetric multivariate leakage correction for MEG connectomes
  publication-title: NeuroImage
– volume: 8
  start-page: 61
  year: 2014
  ident: bb0105
  article-title: Directed transfer function is not influenced by volume conduction—inexpedient pre-processing should be avoided
  publication-title: Front. Comput. Neurosci.
– volume: 54
  start-page: 875
  year: 2011
  end-page: 891
  ident: bb0215
  article-title: Network modelling methods for FMRI
  publication-title: NeuroImage
– volume: 100
  start-page: 234101
  year: 2008
  ident: bb0165
  article-title: Robustly estimating the flow direction of information in complex physical systems
  publication-title: Phys. Rev. Lett.
– start-page: 302
  year: 1999
  end-page: 305
  ident: bb0195
  article-title: Functional neuroimaging by Synthetic Aperture Magnetometry (SAM)
  publication-title: Recent Advances in Biomagnetism
– volume: 53
  start-page: 1357
  year: 2006
  end-page: 1363
  ident: bb0050
  article-title: Modified beamformers for coherent source region suppresion
  publication-title: IEEE Trans. Biomed. Eng.
– volume: 17
  start-page: 666
  year: 2013
  end-page: 682
  ident: bb0220
  article-title: Functional connectomics from resting-state fMRI
  publication-title: Trends Cogn. Sci.
– volume: 15
  start-page: 683
  year: 2014
  end-page: 695
  ident: bb0225
  article-title: Modern network science of neurological disorders
  publication-title: Nat. Rev. Neurosci.
– volume: 80
  start-page: 190
  year: 2013
  end-page: 201
  ident: bb0120
  article-title: Adding dynamics to the human connectome project with MEG
  publication-title: NeuroImage
– volume: 49
  start-page: 3161
  year: 2010
  end-page: 3174
  ident: bb0095
  article-title: Identifying true cortical interactions in meg using the nulling beamformer
  publication-title: NeuroImage
– volume: 91
  start-page: 282
  year: 2014
  end-page: 299
  ident: bb0040
  article-title: Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity
  publication-title: NeuroImage
– volume: 106
  start-page: 2888
  year: 2011
  end-page: 2895
  ident: bb0100
  article-title: How reliable are the functional connectivity networks of MEG in resting states?
  publication-title: J. Neurophys.
– volume: 84
  start-page: 463
  year: 2001
  end-page: 474
  ident: bb0015
  article-title: Partial directed coherence: a new concept in neural structure determination
  publication-title: Biol. Cybern.
– volume: 61
  start-page: 680
  year: 2014
  end-page: 693
  ident: bb0175
  article-title: Measuring time-varying information flow for scalp EEG signals: orthogonalized partial directed coherence
  publication-title: IEEE Trans. Biomed. Eng.
– volume: 79
  start-page: 172
  year: 2013
  end-page: 183
  ident: bb0145
  article-title: Frequency specific interactions of MEG resting state activity within and across brain networks as revealed by the multivariate interaction measure
  publication-title: NeuroImage
– volume: 27
  start-page: 1226
  year: 2005
  end-page: 1238
  ident: bb0190
  article-title: Feature selection based on mutual information: criteria of max-dependency, max-relevance and min-redundancy
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
– volume: 23
  start-page: 7
  year: 2013
  end-page: 18
  ident: bb0255
  article-title: Structure out of chaos: functional brain network analysis with EEG, MEG and functional MRI
  publication-title: Eur. Neuropsychopharmacol.
– volume: 10
  year: 2014
  ident: bb0070
  article-title: Mechanisms of zero-lag synchronization in cortical motifs
  publication-title: PLoS Comput. Biol.
– volume: 123
  start-page: 1067
  year: 2012
  end-page: 1087
  ident: bb0230
  article-title: The organization of physiological brain networks
  publication-title: Clin. Neurophysiol.
– volume: 80
  start-page: 62
  year: 2013
  end-page: 97
  ident: bb0250
  article-title: The WU-Minn human Connectome project: an overview
  publication-title: NeuroImage
– volume: 47
  start-page: 1460
  year: 2009
  end-page: 1468
  ident: bb0065
  article-title: Reproducability of graph metrics of human brain functional networks
  publication-title: NeuroImage
– volume: 32
  start-page: 228
  year: 2006
  end-page: 237
  ident: bb0140
  article-title: Partial correlation for functional brain interactivity investigation in functional MRI
  publication-title: NeuroImage
– volume: 28
  start-page: 143
  year: 2007
  end-page: 157
  ident: bb0005
  article-title: Comparison of different cortical connectivity estimators for high-resolution EEG recordings
  publication-title: Hum. Brain Mapp.
– volume: 107
  start-page: 6040
  year: 2010
  end-page: 6045
  ident: bb0055
  article-title: Temporal dynamics of spontaneous MEG activity in brain networks
  publication-title: Proc. Natl. Acad. Sci. U. S. A.
– volume: 62
  start-page: 530
  year: 2012
  end-page: 541
  ident: bb0130
  article-title: Inferring task-related networks using independent component analysis in magnetoencephalography
  publication-title: NeuroImage
– volume: 44
  start-page: 867
  year: 1997
  end-page: 880
  ident: bb0260
  article-title: Localization of brain electrical activity via linearly constrained minimum variance spatial filtering
  publication-title: IEEE Trans. Biomed. Eng.
– volume: 34
  start-page: 1454
  year: 2007
  end-page: 1465
  ident: bb0025
  article-title: Beamformer reconstruction of correlated sources using a modified source model
  publication-title: NeuroImage
– volume: 37
  start-page: 424
  year: 1969
  end-page: 438
  ident: bb0075
  article-title: Investigating causal relations by econometric models and cross-spectral methods
  publication-title: Econometrica
– volume: 15
  start-page: 884
  year: 2012
  end-page: 890
  ident: bb0090
  article-title: Large-scale cortical correlation structure of spontaneous oscillatory activity
  publication-title: Nat. Neurosci.
– volume: 7
  year: 2012
  ident: bb0280
  article-title: Time-delayed mutual information of the phase as a measure of functional connectivity
  publication-title: PLoS ONE
– volume: 63
  start-page: 910
  year: 2012
  end-page: 920
  ident: bb0035
  article-title: Measuring functional connectivity in MEG: a multivariate approach insensitive to linear source leakage
  publication-title: NeuroImage
– volume: 55
  start-page: 1548
  year: 2011
  end-page: 1565
  ident: bb0265
  article-title: An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample bias
  publication-title: NeuroImage
– volume: 74
  start-page: 231
  year: 2013
  end-page: 244
  ident: bb0010
  article-title: A note on the phase locking value and its properties
  publication-title: NeuroImage
– volume: 96
  start-page: 88
  year: 2014
  end-page: 94
  ident: bb0135
  article-title: Graph theoretical analysis of resting-state MEG data: identifying interhemispheric connectivity and the default mode
  publication-title: NeuroImage
– volume: 97
  start-page: 296
  year: 2014
  end-page: 307
  ident: bb0245
  article-title: Structural degress predicts functional network connectivity: a multimodal resting-state fMRI and MEG study
  publication-title: NeuroImage
– volume: 8
  start-page: 194
  year: 1999
  end-page: 208
  ident: bb0110
  article-title: Measuring phase synchrony in brain signals
  publication-title: Hum. Brain Mapp.
– volume: 371
  start-page: 20110616
  year: 2013
  ident: bb0125
  article-title: Time-variant partial directed coherence for analysing connectivity: a methodological study
  publication-title: Phil. Trans. R. Soc. A
– volume: 108
  start-page: 16783
  year: 2011
  end-page: 16788
  ident: bb0030
  article-title: Investigating the electrophysiological basis of resting state networks using magnetoencephalography
  publication-title: Proc. Natl. Acad. Sci. U. S. A.
– volume: 9
  year: 2014
  ident: bb0080
  article-title: Reproducability of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (WPLI) derived from high resolution EEG
  publication-title: PLoS ONE
– volume: 6
  start-page: 2125
  year: 2012
  end-page: 2149
  ident: bb0150
  article-title: The graphical lasso: new insights and alternatives
  publication-title: Electron. J. Stat.
– volume: 115
  start-page: 2292
  year: 2004
  end-page: 2307
  ident: bb0160
  article-title: Identifying true brain interaction from EEG using the imaginary part of coherency
  publication-title: J. Clin. Neurophysiol.
– volume: 385
  start-page: 157
  year: 1997
  end-page: 161
  ident: bb0200
  article-title: Visuomotor integration is associated with zero time-lag synchronization among cortical areas
  publication-title: Nature
– volume: 36
  start-page: 4604
  year: 2015
  end-page: 4621
  ident: bb0275
  article-title: A geometric correction scheme for spatial leakage effects in MEG/EEG seed-based functional connectivity mapping
  publication-title: Hum. Brain Mapp.
– volume: 115
  start-page: 85
  year: 2015
  end-page: 95
  ident: bb0170
  article-title: Dynamic recruitment of resting state sub-networks
  publication-title: NeuroImage
– volume: 28
  start-page: 1178
  year: 2007
  end-page: 1193
  ident: bb0235
  article-title: Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources
  publication-title: Hum. Brain Mapp.
– volume: 35
  start-page: 1642
  year: 2014
  end-page: 1653
  ident: bb0085
  article-title: A framework for the design of flexible cross-talk functions for spatial filtering of eeg/meg data: DeFleCT
  publication-title: Hum. Brain Mapp.
– volume: 57
  start-page: 1466
  year: 2011
  end-page: 1479
  ident: bb0285
  article-title: MEG beamforming using Bayesian PCA for adaptive data covariance matrix regularization
  publication-title: NeuroImage
– volume: 10
  start-page: 2429
  year: 2000
  end-page: 2439
  ident: bb0115
  article-title: Studying single-trials of phase synchronous activity in the brain
  publication-title: Int. J. Bifurcation Chaos
– year: 2015
  ident: bb0060
  article-title: A dynamic core network and global efficiency in the resting human brain
  publication-title: Cereb. Cortex
– volume: 8
  start-page: 405
  year: 2014
  ident: bb0270
  article-title: A systematic framework for functional connectivity measures
  publication-title: Front. Neurosci.
– volume: 3
  start-page: e01867
  year: 2014
  ident: bb0020
  article-title: Fast transient networks in spontaneous human brain activity
  publication-title: eLIFE
– volume: 16
  start-page: 219
  year: 2012
  end-page: 230
  ident: bb0185
  article-title: Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs
  publication-title: Trends Cogn. Sci.
– volume: 4
  start-page: 812
  year: 2014
  end-page: 825
  ident: bb0155
  article-title: Intrinsic coupling modes in source-reconstructed electroencephalography
  publication-title: Brain Connectivity
– volume: 27
  start-page: 1226
  year: 2005
  ident: 10.1016/j.neuroimage.2016.05.070_bb0190
  article-title: Feature selection based on mutual information: criteria of max-dependency, max-relevance and min-redundancy
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
  doi: 10.1109/TPAMI.2005.159
– volume: 37
  start-page: 424
  year: 1969
  ident: 10.1016/j.neuroimage.2016.05.070_bb0075
  article-title: Investigating causal relations by econometric models and cross-spectral methods
  publication-title: Econometrica
  doi: 10.2307/1912791
– volume: 4
  start-page: 812
  year: 2014
  ident: 10.1016/j.neuroimage.2016.05.070_bb0155
  article-title: Intrinsic coupling modes in source-reconstructed electroencephalography
  publication-title: Brain Connectivity
  doi: 10.1089/brain.2014.0280
– volume: 32
  start-page: 228
  year: 2006
  ident: 10.1016/j.neuroimage.2016.05.070_bb0140
  article-title: Partial correlation for functional brain interactivity investigation in functional MRI
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2005.12.057
– volume: 7
  year: 2012
  ident: 10.1016/j.neuroimage.2016.05.070_bb0280
  article-title: Time-delayed mutual information of the phase as a measure of functional connectivity
  publication-title: PLoS ONE
  doi: 10.1371/journal.pone.0044633
– volume: 74
  start-page: 231
  year: 2013
  ident: 10.1016/j.neuroimage.2016.05.070_bb0010
  article-title: A note on the phase locking value and its properties
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2013.02.008
– volume: 15
  start-page: 884
  year: 2012
  ident: 10.1016/j.neuroimage.2016.05.070_bb0090
  article-title: Large-scale cortical correlation structure of spontaneous oscillatory activity
  publication-title: Nat. Neurosci.
  doi: 10.1038/nn.3101
– volume: 235
  start-page: 341
  year: 1997
  ident: 10.1016/j.neuroimage.2016.05.070_bb0180
  article-title: Detecting phase synchronization in noisy systems
  publication-title: Phys. Lett. A
  doi: 10.1016/S0375-9601(97)00635-X
– volume: 123
  start-page: 1067
  year: 2012
  ident: 10.1016/j.neuroimage.2016.05.070_bb0230
  article-title: The organization of physiological brain networks
  publication-title: Clin. Neurophysiol.
  doi: 10.1016/j.clinph.2012.01.011
– volume: 17
  start-page: 666
  year: 2013
  ident: 10.1016/j.neuroimage.2016.05.070_bb0220
  article-title: Functional connectomics from resting-state fMRI
  publication-title: Trends Cogn. Sci.
  doi: 10.1016/j.tics.2013.09.016
– start-page: 302
  year: 1999
  ident: 10.1016/j.neuroimage.2016.05.070_bb0195
  article-title: Functional neuroimaging by Synthetic Aperture Magnetometry (SAM)
– volume: 8
  start-page: 405
  year: 2014
  ident: 10.1016/j.neuroimage.2016.05.070_bb0270
  article-title: A systematic framework for functional connectivity measures
  publication-title: Front. Neurosci.
  doi: 10.3389/fnins.2014.00405
– volume: 23
  start-page: 7
  year: 2013
  ident: 10.1016/j.neuroimage.2016.05.070_bb0255
  article-title: Structure out of chaos: functional brain network analysis with EEG, MEG and functional MRI
  publication-title: Eur. Neuropsychopharmacol.
  doi: 10.1016/j.euroneuro.2012.10.010
– volume: 44
  start-page: 867
  year: 1997
  ident: 10.1016/j.neuroimage.2016.05.070_bb0260
  article-title: Localization of brain electrical activity via linearly constrained minimum variance spatial filtering
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/10.623056
– volume: 63
  start-page: 910
  year: 2012
  ident: 10.1016/j.neuroimage.2016.05.070_bb0035
  article-title: Measuring functional connectivity in MEG: a multivariate approach insensitive to linear source leakage
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2012.03.048
– volume: 47
  start-page: 1460
  year: 2009
  ident: 10.1016/j.neuroimage.2016.05.070_bb0065
  article-title: Reproducability of graph metrics of human brain functional networks
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2009.05.035
– volume: 97
  start-page: 296
  year: 2014
  ident: 10.1016/j.neuroimage.2016.05.070_bb0245
  article-title: Structural degress predicts functional network connectivity: a multimodal resting-state fMRI and MEG study
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2014.04.038
– volume: 107
  start-page: 6040
  year: 2010
  ident: 10.1016/j.neuroimage.2016.05.070_bb0055
  article-title: Temporal dynamics of spontaneous MEG activity in brain networks
  publication-title: Proc. Natl. Acad. Sci. U. S. A.
  doi: 10.1073/pnas.0913863107
– volume: 106
  start-page: 2888
  year: 2011
  ident: 10.1016/j.neuroimage.2016.05.070_bb0100
  article-title: How reliable are the functional connectivity networks of MEG in resting states?
  publication-title: J. Neurophys.
  doi: 10.1152/jn.00335.2011
– volume: 115
  start-page: 2292
  year: 2004
  ident: 10.1016/j.neuroimage.2016.05.070_bb0160
  article-title: Identifying true brain interaction from EEG using the imaginary part of coherency
  publication-title: J. Clin. Neurophysiol.
  doi: 10.1016/j.clinph.2004.04.029
– volume: 62
  start-page: 530
  year: 2012
  ident: 10.1016/j.neuroimage.2016.05.070_bb0130
  article-title: Inferring task-related networks using independent component analysis in magnetoencephalography
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2012.04.046
– volume: 54
  start-page: 875
  year: 2011
  ident: 10.1016/j.neuroimage.2016.05.070_bb0215
  article-title: Network modelling methods for FMRI
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2010.08.063
– volume: 28
  start-page: 143
  year: 2007
  ident: 10.1016/j.neuroimage.2016.05.070_bb0005
  article-title: Comparison of different cortical connectivity estimators for high-resolution EEG recordings
  publication-title: Hum. Brain Mapp.
  doi: 10.1002/hbm.20263
– volume: 8
  start-page: 61
  year: 2014
  ident: 10.1016/j.neuroimage.2016.05.070_bb0105
  article-title: Directed transfer function is not influenced by volume conduction—inexpedient pre-processing should be avoided
  publication-title: Front. Comput. Neurosci.
  doi: 10.3389/fncom.2014.00061
– volume: 30
  start-page: 1857
  year: 2009
  ident: 10.1016/j.neuroimage.2016.05.070_bb0205
  article-title: Source connectivity analysis with MEG and EEG
  publication-title: Hum. Brain Mapp.
  doi: 10.1002/hbm.20745
– volume: 9
  year: 2014
  ident: 10.1016/j.neuroimage.2016.05.070_bb0080
  article-title: Reproducability of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (WPLI) derived from high resolution EEG
  publication-title: PLoS ONE
  doi: 10.1371/journal.pone.0108648
– volume: 49
  start-page: 3161
  year: 2010
  ident: 10.1016/j.neuroimage.2016.05.070_bb0095
  article-title: Identifying true cortical interactions in meg using the nulling beamformer
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2009.10.078
– volume: 24
  start-page: 248
  year: 2005
  ident: 10.1016/j.neuroimage.2016.05.070_bb0210
  article-title: Variability in fMRI: a re-examination of inter-session differences
  publication-title: Hum. Brain Mapp.
  doi: 10.1002/hbm.20080
– volume: 10
  year: 2014
  ident: 10.1016/j.neuroimage.2016.05.070_bb0070
  article-title: Mechanisms of zero-lag synchronization in cortical motifs
  publication-title: PLoS Comput. Biol.
  doi: 10.1371/journal.pcbi.1003548
– volume: 371
  start-page: 20110616
  year: 2013
  ident: 10.1016/j.neuroimage.2016.05.070_bb0125
  article-title: Time-variant partial directed coherence for analysing connectivity: a methodological study
  publication-title: Phil. Trans. R. Soc. A
  doi: 10.1098/rsta.2011.0616
– volume: 6
  start-page: 2125
  year: 2012
  ident: 10.1016/j.neuroimage.2016.05.070_bb0150
  article-title: The graphical lasso: new insights and alternatives
  publication-title: Electron. J. Stat.
  doi: 10.1214/12-EJS740
– volume: 15
  start-page: 683
  year: 2014
  ident: 10.1016/j.neuroimage.2016.05.070_bb0225
  article-title: Modern network science of neurological disorders
  publication-title: Nat. Rev. Neurosci.
  doi: 10.1038/nrn3801
– volume: 55
  start-page: 1548
  year: 2011
  ident: 10.1016/j.neuroimage.2016.05.070_bb0265
  article-title: An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample bias
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2011.01.055
– volume: 117
  start-page: 439
  year: 2015
  ident: 10.1016/j.neuroimage.2016.05.070_bb0045
  article-title: A symmetric multivariate leakage correction for MEG connectomes
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2015.03.071
– volume: 3
  start-page: e01867
  year: 2014
  ident: 10.1016/j.neuroimage.2016.05.070_bb0020
  article-title: Fast transient networks in spontaneous human brain activity
  publication-title: eLIFE
  doi: 10.7554/eLife.01867
– volume: 80
  start-page: 62
  year: 2013
  ident: 10.1016/j.neuroimage.2016.05.070_bb0250
  article-title: The WU-Minn human Connectome project: an overview
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2013.05.041
– volume: 96
  start-page: 88
  year: 2014
  ident: 10.1016/j.neuroimage.2016.05.070_bb0135
  article-title: Graph theoretical analysis of resting-state MEG data: identifying interhemispheric connectivity and the default mode
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2014.03.065
– volume: 385
  start-page: 157
  year: 1997
  ident: 10.1016/j.neuroimage.2016.05.070_bb0200
  article-title: Visuomotor integration is associated with zero time-lag synchronization among cortical areas
  publication-title: Nature
  doi: 10.1038/385157a0
– volume: 115
  start-page: 85
  year: 2015
  ident: 10.1016/j.neuroimage.2016.05.070_bb0170
  article-title: Dynamic recruitment of resting state sub-networks
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2015.04.030
– volume: 10
  start-page: 2429
  year: 2000
  ident: 10.1016/j.neuroimage.2016.05.070_bb0115
  article-title: Studying single-trials of phase synchronous activity in the brain
  publication-title: Int. J. Bifurcation Chaos
  doi: 10.1142/S0218127400001560
– volume: 100
  start-page: 234101
  year: 2008
  ident: 10.1016/j.neuroimage.2016.05.070_bb0165
  article-title: Robustly estimating the flow direction of information in complex physical systems
  publication-title: Phys. Rev. Lett.
  doi: 10.1103/PhysRevLett.100.234101
– volume: 16
  start-page: 219
  year: 2012
  ident: 10.1016/j.neuroimage.2016.05.070_bb0185
  article-title: Discovering oscillatory interaction networks with M/EEG: challenges and breakthroughs
  publication-title: Trends Cogn. Sci.
  doi: 10.1016/j.tics.2012.02.004
– year: 2015
  ident: 10.1016/j.neuroimage.2016.05.070_bb0060
  article-title: A dynamic core network and global efficiency in the resting human brain
  publication-title: Cereb. Cortex
– volume: 132
  start-page: 213
  year: 2009
  ident: 10.1016/j.neuroimage.2016.05.070_bb0240
  article-title: Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease
  publication-title: Brain
  doi: 10.1093/brain/awn262
– volume: 80
  start-page: 190
  year: 2013
  ident: 10.1016/j.neuroimage.2016.05.070_bb0120
  article-title: Adding dynamics to the human connectome project with MEG
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2013.05.056
– volume: 36
  start-page: 4604
  year: 2015
  ident: 10.1016/j.neuroimage.2016.05.070_bb0275
  article-title: A geometric correction scheme for spatial leakage effects in MEG/EEG seed-based functional connectivity mapping
  publication-title: Hum. Brain Mapp.
  doi: 10.1002/hbm.22943
– volume: 8
  start-page: 194
  year: 1999
  ident: 10.1016/j.neuroimage.2016.05.070_bb0110
  article-title: Measuring phase synchrony in brain signals
  publication-title: Hum. Brain Mapp.
  doi: 10.1002/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C
– volume: 84
  start-page: 463
  year: 2001
  ident: 10.1016/j.neuroimage.2016.05.070_bb0015
  article-title: Partial directed coherence: a new concept in neural structure determination
  publication-title: Biol. Cybern.
  doi: 10.1007/PL00007990
– volume: 57
  start-page: 1466
  year: 2011
  ident: 10.1016/j.neuroimage.2016.05.070_bb0285
  article-title: MEG beamforming using Bayesian PCA for adaptive data covariance matrix regularization
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2011.04.041
– volume: 53
  start-page: 1357
  year: 2006
  ident: 10.1016/j.neuroimage.2016.05.070_bb0050
  article-title: Modified beamformers for coherent source region suppresion
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2006.873752
– volume: 34
  start-page: 1454
  year: 2007
  ident: 10.1016/j.neuroimage.2016.05.070_bb0025
  article-title: Beamformer reconstruction of correlated sources using a modified source model
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2006.11.012
– volume: 91
  start-page: 282
  year: 2014
  ident: 10.1016/j.neuroimage.2016.05.070_bb0040
  article-title: Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2013.12.066
– volume: 108
  start-page: 16783
  year: 2011
  ident: 10.1016/j.neuroimage.2016.05.070_bb0030
  article-title: Investigating the electrophysiological basis of resting state networks using magnetoencephalography
  publication-title: Proc. Natl. Acad. Sci. U. S. A.
  doi: 10.1073/pnas.1112685108
– volume: 35
  start-page: 1642
  year: 2014
  ident: 10.1016/j.neuroimage.2016.05.070_bb0085
  article-title: A framework for the design of flexible cross-talk functions for spatial filtering of eeg/meg data: DeFleCT
  publication-title: Hum. Brain Mapp.
  doi: 10.1002/hbm.22279
– volume: 79
  start-page: 172
  year: 2013
  ident: 10.1016/j.neuroimage.2016.05.070_bb0145
  article-title: Frequency specific interactions of MEG resting state activity within and across brain networks as revealed by the multivariate interaction measure
  publication-title: NeuroImage
  doi: 10.1016/j.neuroimage.2013.04.062
– volume: 28
  start-page: 1178
  year: 2007
  ident: 10.1016/j.neuroimage.2016.05.070_bb0235
  article-title: Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources
  publication-title: Hum. Brain Mapp.
  doi: 10.1002/hbm.20346
– volume: 61
  start-page: 680
  year: 2014
  ident: 10.1016/j.neuroimage.2016.05.070_bb0175
  article-title: Measuring time-varying information flow for scalp EEG signals: orthogonalized partial directed coherence
  publication-title: IEEE Trans. Biomed. Eng.
  doi: 10.1109/TBME.2013.2286394
SSID ssj0009148
Score 2.6272123
Snippet MEG offers dynamic and spectral resolution for resting-state connectivity which is unavailable in fMRI. However, there are a wide range of available network...
SourceID pubmedcentral
proquest
pubmed
crossref
elsevier
SourceType Open Access Repository
Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 284
SubjectTerms Adult
Algorithms
Biomedical research
Cerebral Cortex - physiology
Connectome
Connectome - methods
Female
Functional connectivity
Humans
Magnetic field spread
Magnetoencephalography - methods
Male
Medical research
MEG
Methods
Nerve Net - physiology
Network analysis
Neurosciences
Reproducibility of Results
Researchers
Rest - physiology
Sensitivity and Specificity
Sensors
Source leakage
Studies
Technical Note
SummonAdditionalLinks – databaseName: Health & Medical Collection
  dbid: 7X7
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LS8QwEA4-QLyIb9cXFbwGm7ZpGzyIyOoirCcX9hbSNMEVbdfdFf--M23a9YXsoadkSjvJzHxJJt8Qcp4JHkaxr6iB-AYLlCyggKojajKIHia2Ea9O8PsPcW8Q3Q_50G24TV1aZeMTK0edlxr3yC8YEtchOVZwNX6jWDUKT1ddCY1lsorUZbj4SobJnHSXRfVVOB7SFDq4TJ46v6viixy9gtVigldc8XdiyeK_w9Nv-Pkzi_JLWLrdJBsOT3rX9QTYIkum2CZrfXdivkPSXvnhTczLCK9IeWpivH73zsOCHBCzaHWdyNOY7KLrMhLeK5bY0tOrXTK47T7e9KirlkA1LApmVHGreC5SpVILD4t9m2mRwwKF29RmVocYiQCfGc3DOLKBr0QoUmZMyKIg1-EeWSnKwhwQD7TMmc50Ehi0caHCLDd54Gufp4ZZv0OSRklSOypxrGjxIpucsWc5V69E9UqfS1Bvh7BWclzTaSwgI5pxkM11UXBwEnz-ArKXrayDFDVUWFD6uBl26Ux7KucTsUPO2mYwSjxpUYUp37EP0igBPOD_9gFfCWgZ3rNfz6RWJUESxIDbBCj62xxrOyAp-PeWYvRUkYMjohWcH_7_6UdkHf-zTpY7Jiuzybs5AXQ1y04rE_oEMr0k2Q
  priority: 102
  providerName: ProQuest
Title How reliable are MEG resting-state connectivity metrics?
URI https://www.clinicalkey.com/#!/content/1-s2.0-S1053811916301914
https://dx.doi.org/10.1016/j.neuroimage.2016.05.070
https://www.ncbi.nlm.nih.gov/pubmed/27262239
https://www.proquest.com/docview/1877799592
https://www.proquest.com/docview/1800701305
https://www.proquest.com/docview/1808705752
https://pubmed.ncbi.nlm.nih.gov/PMC5056955
Volume 138
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEF5EQbyIb-uLCF5j89okiwfRUq2PFvEBvS2bzS5GNJVa8eZvdybZRKsiBQ9taHcntJPZmW_ZmW8I2UsY9YPQEbaC-AYblMSzAVUHtkogeqhQB7Q4we_2ws5dcN6n_SnSqmphMK3S-P7Spxfe2nzTNNpsPmdZ8waQAYQb2G-EYKSsaGaN7HVg0_vvn2keMFCWw1Hfxtkmm6fM8So4I7MnWLmY5BUWHJ7Ytvj3EPUTgn7PpPwSmk4WyLzBlNZR-bMXyZTKl8hs15yaL5O4M3izhuoxwzIpSwyV1W2fWtiUA-KWXZQUWRITXmTZSsJ6wjZb8uVwhdydtG9bHdt0TLAlbAxGtqBa0JTFQsQaXm7o6ESyFDYpVMc60dLHaAQYTUnqh4H2HMF8FrtK-W7gpdJfJdP5IFfrxGIUsJdMZOQpXOdM-EmqUs-RDo2Vq50GiSolcWnoxLGrxSOv8sYe-Kd6OaqXO5SDehvErSWfS0qNCWRY9Rx4VTIKTo6D359A9qCWHTOtCaW3qsfOzfJ-4S6yKCJTm9cgu_UwLEw8bRG5GrziHKRSAohA_5wD_hIQM9xnrbSkWiVe5IWA3RgoeszG6glIDD4-kmf3BUE4olpG6ca__vgmmcNPZT7dFpkeDV_VNgCwUbJTrDB4j_rRDpk5al1fXuH17KLTg-txu3d1_QGv9DWv
linkProvider Elsevier
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1NbxMxEB2VIgEXxGcJFDASHC3W3vXuWghVVWlJadNTK-VmvF5bBLWbkqSq-FP8Rmb2KxRQlUsPOa1nlYxn37yNZ94AvC20ipM0stxjfsMXlEJyZNUJ9wVmD5-GRNUn-KOjdHiSfBmr8Rr86nphqKyyw8QaqMupo__I3wsSriNxLLl1_oPT1Cg6Xe1GaDRhceB_XuIr2_zj_ifc33dS7u0e7wx5O1WAOyTPC25VsKrUubV5wI9Io1A4XSKRVyEPRXAxITbyGO9UnCZBRlbHOhfexyKRpYvxvrfgNibeiEoIs3G2FPkVSdN6p2KeC6HbyqGmnqzWp5ycIUpQQVla64XSiOT_p8N_6e7fVZt_pMG9B3C_5a9suwm4h7Dmq0dwZ9Se0D-GfDi9ZDN_OqGWLGZnno12PzMaAII5ktftS8xRcY1rxlawMxrp5eZbT-DkRvz4FNaraeWfAcNdVcIVLpOeMEXbuCh9KSMXqdyLEA0g65xkXCtdThM0Tk1Xo_bdLN1ryL0mUgbdOwDRW5438h0r2OhuH0zXnoqAajDHrGD7obdtKUxDTVa03uy23bRQMjfLwB_Am_4yggCd7NjKTy9oDck2IR1R165BbEZ2jvfZaCKpd4nMZIo8UaOjr8RYv4BEyK9eqSbfajFyYtBaqefXf_XXcHd4PDo0h_tHBy_gHv3mplBvE9YXswv_EpndonhVP04Mvt708_sblhJiWg
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Nb9QwELVKkSouiG8WChgJjlZjO05iIVQh2mVL2YoDlfZmHMcWi9ps2d2q4q_x65iJkywFVO2lh5zsiZKxZ-YlnnlDyKtSK5lmiWUe4ht8oJSCAapOmS8hevgspKo5wR8fZaPj9ONETTbIr64WBtMqO5_YOOpq5vAf-Q5H4jokxxI7oU2L-Lw33D37wbCDFJ60du004hY59D8v4PNt8fZgD9b6tRDD_S_vR6ztMMAcAOklsypYVenC2iLAxbMklE5XAOpVKEIZnETvDZjGOyWzNIjEaqkL7r3kqaichPveIDdzCWETbCmf5CvCX57GMjwlWcG5brOIYm5Zw1U5PQWPgcllWcMdiu2S_x8a_4W-f2dw_hESh3fI7RbL0ndx890lG76-R7bG7Wn9fVKMZhd07k-mWJ5F7dzT8f4His1AIF6yppSJOky0cbGFBT3F9l5usfuAHF-LHh-SzXpW-8eEwgor7kqXC4_-RVtZVr4SiUtU4XlIBiTvlGRcS2OO3TROTJev9t2s1GtQvSZRBtQ7ILyXPItUHmvI6G4dTFeqCs7VQLxZQ_ZNL9vCmQhT1pTe7pbdtG5lYVZGMCAv-2FwCHjKY2s_O8c5SOEE0ERdOQf8NCB1uM-juJN6lYhcZIAZNSj60h7rJyAh-eWRevqtISZHNK2VenL1o78gW2C55tPB0eFTcgtfOebsbZPN5fzcPwOQtyyfN9ZEydfrNt_f_01mkA
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=How+reliable+are+MEG+resting-state+connectivity+metrics%3F&rft.jtitle=NeuroImage+%28Orlando%2C+Fla.%29&rft.au=Colclough%2C+G+L&rft.au=Woolrich%2C+M+W&rft.au=Tewarie%2C+P+K&rft.au=Brookes%2C+MJ&rft.date=2016-09-01&rft.issn=1053-8119&rft.volume=138&rft.spage=284&rft.epage=293&rft_id=info:doi/10.1016%2Fj.neuroimage.2016.05.070&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1053-8119&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1053-8119&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1053-8119&client=summon