Single‐subject morphological brain networks: connectivity mapping, topological characterization and test–retest reliability

Introduction Structural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological features among regions, however, are not well understood. Here, we constructed individual‐level morphological brain networks and systematic...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Brain and behavior Ročník 6; číslo 4; s. e00448 - n/a
Hlavní autori: Wang, Hao, Jin, Xiaoqing, Zhang, Ye, Wang, Jinhui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States John Wiley & Sons, Inc 01.04.2016
John Wiley and Sons Inc
Predmet:
Adult
Brain - anatomy & histology
Brain - diagnostic imaging
Brain network
Brain research
Female
Gray Matter - anatomy & histology
Gray Matter - diagnostic imaging
gray matter volume
hub
Humans
Labeling
Magnetic Resonance Imaging - methods
Magnetic Resonance Imaging - standards
Male
Medical imaging
Morphology
Nerve Net - anatomy & histology
Nerve Net - diagnostic imaging
Original Research
Probability distribution
reliability
Reproducibility of Results
structural MRI
Studies
Young Adult
Beijing China
China
gray matter volume
structural MRI
hub
Brain network
reliability
ISSN:2162-3279, 2162-3279
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract Introduction Structural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological features among regions, however, are not well understood. Here, we constructed individual‐level morphological brain networks and systematically examined their topological organization and long‐term test–retest reliability under different analytical schemes of spatial smoothing, brain parcellation, and network type. Methods This study included 57 healthy participants and all participants completed two MRI scan sessions. Individual morphological brain networks were constructed by estimating interregional similarity in the distribution of regional gray matter volume in terms of the Kullback–Leibler divergence measure. Graph‐based global and nodal network measures were then calculated, followed by the statistical comparison and intra‐class correlation analysis. Results The morphological brain networks were highly reproducible between sessions with significantly larger similarities for interhemispheric connections linking bilaterally homotopic regions. Further graph‐based analyses revealed that the morphological brain networks exhibited nonrandom topological organization of small‐worldness, high parallel efficiency and modular architecture regardless of the analytical choices of spatial smoothing, brain parcellation and network type. Moreover, several paralimbic and association regions were consistently revealed to be potential hubs. Nonetheless, the three studied factors particularly spatial smoothing significantly affected quantitative characterization of morphological brain networks. Further examination of long‐term reliability revealed that all the examined network topological properties showed fair to excellent reliability irrespective of the analytical strategies, but performing spatial smoothing significantly improved reliability. Interestingly, nodal centralities were positively correlated with their reliabilities, and nodal degree and efficiency outperformed nodal betweenness with respect to reliability. Conclusions Our findings support single‐subject morphological network analysis as a meaningful and reliable method to characterize structural organization of the human brain; this method thus opens a new avenue toward understanding the substrate of intersubject variability in behavior and function and establishing morphological network biomarkers in brain disorders. We proposed a method to construct individual‐level morphological brain networks from structural MRI data. We demonstrated that morphological brain networks derived from this method were specifically organized, test–retest reliable and dependent on different analytic strategies of data preprocessing and network construction methods.
AbstractList Structural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological features among regions, however, are not well understood. Here, we constructed individual-level morphological brain networks and systematically examined their topological organization and long-term test-retest reliability under different analytical schemes of spatial smoothing, brain parcellation, and network type.INTRODUCTIONStructural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological features among regions, however, are not well understood. Here, we constructed individual-level morphological brain networks and systematically examined their topological organization and long-term test-retest reliability under different analytical schemes of spatial smoothing, brain parcellation, and network type.This study included 57 healthy participants and all participants completed two MRI scan sessions. Individual morphological brain networks were constructed by estimating interregional similarity in the distribution of regional gray matter volume in terms of the Kullback-Leibler divergence measure. Graph-based global and nodal network measures were then calculated, followed by the statistical comparison and intra-class correlation analysis.METHODSThis study included 57 healthy participants and all participants completed two MRI scan sessions. Individual morphological brain networks were constructed by estimating interregional similarity in the distribution of regional gray matter volume in terms of the Kullback-Leibler divergence measure. Graph-based global and nodal network measures were then calculated, followed by the statistical comparison and intra-class correlation analysis.The morphological brain networks were highly reproducible between sessions with significantly larger similarities for interhemispheric connections linking bilaterally homotopic regions. Further graph-based analyses revealed that the morphological brain networks exhibited nonrandom topological organization of small-worldness, high parallel efficiency and modular architecture regardless of the analytical choices of spatial smoothing, brain parcellation and network type. Moreover, several paralimbic and association regions were consistently revealed to be potential hubs. Nonetheless, the three studied factors particularly spatial smoothing significantly affected quantitative characterization of morphological brain networks. Further examination of long-term reliability revealed that all the examined network topological properties showed fair to excellent reliability irrespective of the analytical strategies, but performing spatial smoothing significantly improved reliability. Interestingly, nodal centralities were positively correlated with their reliabilities, and nodal degree and efficiency outperformed nodal betweenness with respect to reliability.RESULTSThe morphological brain networks were highly reproducible between sessions with significantly larger similarities for interhemispheric connections linking bilaterally homotopic regions. Further graph-based analyses revealed that the morphological brain networks exhibited nonrandom topological organization of small-worldness, high parallel efficiency and modular architecture regardless of the analytical choices of spatial smoothing, brain parcellation and network type. Moreover, several paralimbic and association regions were consistently revealed to be potential hubs. Nonetheless, the three studied factors particularly spatial smoothing significantly affected quantitative characterization of morphological brain networks. Further examination of long-term reliability revealed that all the examined network topological properties showed fair to excellent reliability irrespective of the analytical strategies, but performing spatial smoothing significantly improved reliability. Interestingly, nodal centralities were positively correlated with their reliabilities, and nodal degree and efficiency outperformed nodal betweenness with respect to reliability.Our findings support single-subject morphological network analysis as a meaningful and reliable method to characterize structural organization of the human brain; this method thus opens a new avenue toward understanding the substrate of intersubject variability in behavior and function and establishing morphological network biomarkers in brain disorders.CONCLUSIONSOur findings support single-subject morphological network analysis as a meaningful and reliable method to characterize structural organization of the human brain; this method thus opens a new avenue toward understanding the substrate of intersubject variability in behavior and function and establishing morphological network biomarkers in brain disorders.
Structural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological features among regions, however, are not well understood. Here, we constructed individual-level morphological brain networks and systematically examined their topological organization and long-term test-retest reliability under different analytical schemes of spatial smoothing, brain parcellation, and network type. This study included 57 healthy participants and all participants completed two MRI scan sessions. Individual morphological brain networks were constructed by estimating interregional similarity in the distribution of regional gray matter volume in terms of the Kullback-Leibler divergence measure. Graph-based global and nodal network measures were then calculated, followed by the statistical comparison and intra-class correlation analysis. The morphological brain networks were highly reproducible between sessions with significantly larger similarities for interhemispheric connections linking bilaterally homotopic regions. Further graph-based analyses revealed that the morphological brain networks exhibited nonrandom topological organization of small-worldness, high parallel efficiency and modular architecture regardless of the analytical choices of spatial smoothing, brain parcellation and network type. Moreover, several paralimbic and association regions were consistently revealed to be potential hubs. Nonetheless, the three studied factors particularly spatial smoothing significantly affected quantitative characterization of morphological brain networks. Further examination of long-term reliability revealed that all the examined network topological properties showed fair to excellent reliability irrespective of the analytical strategies, but performing spatial smoothing significantly improved reliability. Interestingly, nodal centralities were positively correlated with their reliabilities, and nodal degree and efficiency outperformed nodal betweenness with respect to reliability. Our findings support single-subject morphological network analysis as a meaningful and reliable method to characterize structural organization of the human brain; this method thus opens a new avenue toward understanding the substrate of intersubject variability in behavior and function and establishing morphological network biomarkers in brain disorders.
IntroductionStructural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological features among regions, however, are not well understood. Here, we constructed individual‐level morphological brain networks and systematically examined their topological organization and long‐term test–retest reliability under different analytical schemes of spatial smoothing, brain parcellation, and network type.MethodsThis study included 57 healthy participants and all participants completed two MRI scan sessions. Individual morphological brain networks were constructed by estimating interregional similarity in the distribution of regional gray matter volume in terms of the Kullback–Leibler divergence measure. Graph‐based global and nodal network measures were then calculated, followed by the statistical comparison and intra‐class correlation analysis.ResultsThe morphological brain networks were highly reproducible between sessions with significantly larger similarities for interhemispheric connections linking bilaterally homotopic regions. Further graph‐based analyses revealed that the morphological brain networks exhibited nonrandom topological organization of small‐worldness, high parallel efficiency and modular architecture regardless of the analytical choices of spatial smoothing, brain parcellation and network type. Moreover, several paralimbic and association regions were consistently revealed to be potential hubs. Nonetheless, the three studied factors particularly spatial smoothing significantly affected quantitative characterization of morphological brain networks. Further examination of long‐term reliability revealed that all the examined network topological properties showed fair to excellent reliability irrespective of the analytical strategies, but performing spatial smoothing significantly improved reliability. Interestingly, nodal centralities were positively correlated with their reliabilities, and nodal degree and efficiency outperformed nodal betweenness with respect to reliability.ConclusionsOur findings support single‐subject morphological network analysis as a meaningful and reliable method to characterize structural organization of the human brain; this method thus opens a new avenue toward understanding the substrate of intersubject variability in behavior and function and establishing morphological network biomarkers in brain disorders.
Introduction Structural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological features among regions, however, are not well understood. Here, we constructed individual‐level morphological brain networks and systematically examined their topological organization and long‐term test–retest reliability under different analytical schemes of spatial smoothing, brain parcellation, and network type. Methods This study included 57 healthy participants and all participants completed two MRI scan sessions. Individual morphological brain networks were constructed by estimating interregional similarity in the distribution of regional gray matter volume in terms of the Kullback–Leibler divergence measure. Graph‐based global and nodal network measures were then calculated, followed by the statistical comparison and intra‐class correlation analysis. Results The morphological brain networks were highly reproducible between sessions with significantly larger similarities for interhemispheric connections linking bilaterally homotopic regions. Further graph‐based analyses revealed that the morphological brain networks exhibited nonrandom topological organization of small‐worldness, high parallel efficiency and modular architecture regardless of the analytical choices of spatial smoothing, brain parcellation and network type. Moreover, several paralimbic and association regions were consistently revealed to be potential hubs. Nonetheless, the three studied factors particularly spatial smoothing significantly affected quantitative characterization of morphological brain networks. Further examination of long‐term reliability revealed that all the examined network topological properties showed fair to excellent reliability irrespective of the analytical strategies, but performing spatial smoothing significantly improved reliability. Interestingly, nodal centralities were positively correlated with their reliabilities, and nodal degree and efficiency outperformed nodal betweenness with respect to reliability. Conclusions Our findings support single‐subject morphological network analysis as a meaningful and reliable method to characterize structural organization of the human brain; this method thus opens a new avenue toward understanding the substrate of intersubject variability in behavior and function and establishing morphological network biomarkers in brain disorders. We proposed a method to construct individual‐level morphological brain networks from structural MRI data. We demonstrated that morphological brain networks derived from this method were specifically organized, test–retest reliable and dependent on different analytic strategies of data preprocessing and network construction methods.
Author Jin, Xiaoqing
Zhang, Ye
Wang, Hao
Wang, Jinhui
AuthorAffiliation 1 Department of Psychology Hangzhou Normal University Hangzhou 311121 China
2 Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121 China
3 Department of Acupuncture and Moxibustion Zhejiang Hospital Hangzhou 310030 China
AuthorAffiliation_xml – name: 3 Department of Acupuncture and Moxibustion Zhejiang Hospital Hangzhou 310030 China
– name: 1 Department of Psychology Hangzhou Normal University Hangzhou 311121 China
– name: 2 Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121 China
Author_xml – sequence: 1
  givenname: Hao
  surname: Wang
  fullname: Wang, Hao
  organization: Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments
– sequence: 2
  givenname: Xiaoqing
  surname: Jin
  fullname: Jin, Xiaoqing
  organization: Zhejiang Hospital
– sequence: 3
  givenname: Ye
  surname: Zhang
  fullname: Zhang, Ye
  organization: Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments
– sequence: 4
  givenname: Jinhui
  surname: Wang
  fullname: Wang, Jinhui
  organization: Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments
BackLink https://www.ncbi.nlm.nih.gov/pubmed/27088054$$D View this record in MEDLINE/PubMed
BookMark eNp1kd1u1DAQhS1UREupxBOgSNxwQRbHdhKHCyRa8SdVQuLn2nKcya4Xx05tp9VyQx8BiTfsk-Cw7VIQ-MYjzTdHZ-bcR3vWWUDoYYEXBcbkWetbumCM30EHpKhITknd7N2q99FRCGucXlkwwvA9tE9qzDku2QH69lHbpYGry-9hategYjY4P66ccUutpMlaL7XNLMQL57-E55ly1iZKn-u4yQY5jmn8aRbduJtQK-mliuD1Vxm1s5m0XRYhxKvLHx7mIvNgtGy1SRoP0N1emgBH1_8h-vz61aeTt_np-zfvTl6e5qrEjOeSN6pkuKooMEl433cVIZL0ddXhXhaScoCuwyWmoKAiXdWkg9R90UNZVkXZ00P0Yqs7Tu0AnQIbvTRi9HqQfiOc1OLPjtUrsXTngtWcENYkgSfXAt6dTWkLMeigwBhpwU1BFDUva1ZROqOP_0LXbvI2rScI4bxsCsrqRD267Whn5SabBCy2gPIuBA-9UDr-OmkyqI0osJjzF3P-Iq372-Ju4EbzH2i-RS-0gc1_OXH84ZjO_E-ed8QD
CitedBy_id crossref_primary_10_3389_fneur_2022_763305
crossref_primary_10_1016_j_neulet_2018_07_029
crossref_primary_10_1002_hbm_25434
crossref_primary_10_1007_s00429_025_02909_5
crossref_primary_10_3389_fneur_2022_834277
crossref_primary_10_1016_j_neuroimage_2022_119009
crossref_primary_10_1007_s11682_022_00747_1
crossref_primary_10_1007_s00234_021_02653_7
crossref_primary_10_1016_j_ebr_2024_100676
crossref_primary_10_1016_j_psychres_2023_115557
crossref_primary_10_1111_cns_70479
crossref_primary_10_1016_j_brainres_2024_148986
crossref_primary_10_3389_fcell_2021_803800
crossref_primary_10_1093_cercor_bhac217
crossref_primary_10_1016_j_tins_2023_11_011
crossref_primary_10_1038_s41593_023_01376_7
crossref_primary_10_1016_j_jad_2022_11_029
crossref_primary_10_1016_j_neuroscience_2024_09_004
crossref_primary_10_1111_eip_13245
crossref_primary_10_1007_s00330_021_08080_9
crossref_primary_10_1038_s41398_020_00873_8
crossref_primary_10_3389_fnins_2023_1089134
crossref_primary_10_1007_s10548_023_00962_z
crossref_primary_10_1002_jmri_28318
crossref_primary_10_1016_j_brainresbull_2023_110714
crossref_primary_10_1016_j_bbr_2025_115787
crossref_primary_10_1016_j_mri_2019_11_003
crossref_primary_10_3389_fnins_2021_630278
crossref_primary_10_1093_cercor_bhad439
crossref_primary_10_1007_s00259_020_04814_x
crossref_primary_10_1186_s12967_023_04164_w
crossref_primary_10_1002_jmri_27499
crossref_primary_10_1016_j_jpsychires_2021_06_019
crossref_primary_10_1002_advs_202400061
crossref_primary_10_1017_S003329172300168X
crossref_primary_10_3389_fnins_2022_952067
crossref_primary_10_1002_sta4_119
crossref_primary_10_1093_psyrad_kkad017
crossref_primary_10_1177_13872877251316794
crossref_primary_10_3389_fnins_2023_1140801
crossref_primary_10_1186_s10194_024_01861_9
crossref_primary_10_1111_cns_14384
crossref_primary_10_1016_j_neuroimage_2024_120688
crossref_primary_10_1002_hbm_25606
crossref_primary_10_1093_cercor_bhae016
crossref_primary_10_1007_s10278_024_01230_7
crossref_primary_10_1093_cercor_bhad450
crossref_primary_10_1093_cercor_bhad178
crossref_primary_10_1093_cercor_bhad211
crossref_primary_10_3390_jcm11185362
crossref_primary_10_3390_brainsci13050803
crossref_primary_10_1007_s11042_024_18817_5
crossref_primary_10_1016_j_neuroimage_2023_120265
crossref_primary_10_3389_fneur_2024_1519397
crossref_primary_10_1002_brb3_3103
crossref_primary_10_3389_fpsyt_2021_777447
crossref_primary_10_1016_j_schres_2019_01_025
crossref_primary_10_3389_fnagi_2021_688113
crossref_primary_10_1007_s12264_024_01262_7
crossref_primary_10_1016_j_jad_2022_02_052
crossref_primary_10_1038_s41531_025_01028_6
crossref_primary_10_3389_fnhum_2018_00204
crossref_primary_10_1038_s41598_022_22387_7
crossref_primary_10_3389_fnhum_2019_00399
crossref_primary_10_1155_2020_3560259
crossref_primary_10_1002_hbm_24863
crossref_primary_10_1016_j_neuroimage_2024_120673
crossref_primary_10_1016_j_neuroimage_2025_121402
crossref_primary_10_1038_s41386_019_0322_y
crossref_primary_10_1002_alz_70095
crossref_primary_10_1002_hbm_25230
crossref_primary_10_1093_brain_awaf151
crossref_primary_10_1016_j_parkreldis_2024_106985
crossref_primary_10_3389_fnins_2021_785595
crossref_primary_10_1016_j_brainresbull_2025_111228
crossref_primary_10_3389_fnins_2021_756868
crossref_primary_10_1093_schbul_sbae069
crossref_primary_10_1007_s11682_025_01026_5
crossref_primary_10_1111_cns_13632
crossref_primary_10_1002_mp_17568
crossref_primary_10_1016_j_ijchp_2025_100564
crossref_primary_10_1007_s00429_023_02616_z
crossref_primary_10_1016_j_heliyon_2024_e28874
crossref_primary_10_1097_QAD_0000000000003759
crossref_primary_10_3389_fcell_2021_631864
crossref_primary_10_1016_j_parkreldis_2020_10_044
crossref_primary_10_1002_mco2_305
crossref_primary_10_1192_bjp_2024_41
crossref_primary_10_1016_j_jpsychires_2024_12_032
crossref_primary_10_3389_fneur_2025_1561555
crossref_primary_10_1093_cercor_bhae039
crossref_primary_10_1007_s00429_020_02200_9
crossref_primary_10_1371_journal_pone_0201243
crossref_primary_10_1016_j_jad_2024_01_173
crossref_primary_10_1093_cercor_bhad231
crossref_primary_10_2337_db21_0600
crossref_primary_10_1002_jmri_27544
crossref_primary_10_1002_hbm_25344
crossref_primary_10_1177_13872877251360259
crossref_primary_10_1007_s10548_023_00964_x
crossref_primary_10_1093_schbul_sbae218
crossref_primary_10_1177_1756286419838673
crossref_primary_10_7717_peerj_3533
crossref_primary_10_3389_fnins_2018_01055
crossref_primary_10_1016_j_neuroscience_2017_10_033
crossref_primary_10_1016_j_neuroimage_2023_120434
crossref_primary_10_1016_j_neuroimage_2024_120893
crossref_primary_10_3389_fonc_2023_1098748
crossref_primary_10_1016_j_bspc_2024_106592
crossref_primary_10_3389_fnagi_2022_873148
crossref_primary_10_1007_s10803_023_06160_x
crossref_primary_10_1016_j_neuroimage_2025_121069
crossref_primary_10_3389_fnins_2024_1381385
crossref_primary_10_1007_s11682_021_00571_z
crossref_primary_10_1186_s13195_024_01448_1
crossref_primary_10_1007_s40846_021_00676_2
crossref_primary_10_1038_s41386_022_01328_y
crossref_primary_10_1002_hbm_24954
crossref_primary_10_1093_nsr_nwaa029
crossref_primary_10_1016_j_neuroscience_2025_07_033
crossref_primary_10_1155_da_6885509
crossref_primary_10_1093_cercor_bhae337
crossref_primary_10_1093_schbul_sbz062
crossref_primary_10_3389_fnhum_2020_00172
crossref_primary_10_1080_10255842_2025_2515477
crossref_primary_10_1016_j_neuroimage_2024_120762
crossref_primary_10_1002_hbm_26450
crossref_primary_10_1002_jnr_25178
crossref_primary_10_1016_j_biopsych_2023_12_025
crossref_primary_10_1093_cercor_bhad372
crossref_primary_10_1016_j_nicl_2020_102552
crossref_primary_10_1162_netn_a_00458
crossref_primary_10_3389_fnins_2020_00344
crossref_primary_10_3389_fnagi_2022_1091829
crossref_primary_10_3389_fnagi_2022_834145
crossref_primary_10_1093_schbul_sbae110
crossref_primary_10_1038_s41598_021_01915_x
crossref_primary_10_1371_journal_pone_0187281
crossref_primary_10_1016_j_neurad_2023_09_007
crossref_primary_10_1016_j_neuroscience_2021_02_008
crossref_primary_10_3389_fimmu_2024_1345843
crossref_primary_10_1007_s11060_016_2328_1
crossref_primary_10_1093_cercor_bhad410
crossref_primary_10_1038_s41398_024_02861_8
Cites_doi 10.1016/j.neuroimage.2007.09.031
10.1093/brain/awp089
10.1016/j.tics.2013.09.012
10.1016/j.neuroimage.2008.09.062
10.1037/0033-2909.86.2.420
10.1016/j.tics.2013.09.004
10.1002/hbm.460030304
10.1146/annurev-clinpsy-040510-143934
10.1523/JNEUROSCI.1929-08.2008
10.1214/aoms/1177704472
10.1007/s00429-005-0041-5
10.1016/j.neuroimage.2013.05.054
10.1093/cercor/8.4.372
10.1038/nrn3801
10.1038/nrn3465
10.1073/pnas.0911855107
10.1016/j.neurobiolaging.2010.06.022
10.1523/JNEUROSCI.3874-05.2006
10.1016/S0169-7439(99)00047-7
10.1038/385313a0
10.1016/j.neuroimage.2006.01.021
10.1111/jcpp.12365
10.1523/JNEUROSCI.3554-12.2013
10.1177/1073858414537560
10.1371/journal.pone.0021935
10.1523/JNEUROSCI.0141-08.2008
10.1016/j.tics.2012.02.001
10.1371/journal.pone.0005226
10.1016/j.neuroimage.2009.11.001
10.1002/mrm.1910340409
10.1038/nmeth.2482
10.1016/j.euroneuro.2014.02.011
10.1016/j.neuroimage.2010.05.045
10.1016/j.neuroimage.2013.09.054
10.1038/nrn3214
10.1016/j.neuroimage.2010.07.025
10.1016/j.neuroimage.2009.12.027
10.1371/journal.pone.0000597
10.1002/hbm.21232
10.1093/cercor/bhn102
10.1002/hbm.20623
10.1214/10-AOS799
10.1016/j.tics.2004.07.008
10.1093/cercor/bhi016
10.1093/cercor/bhr221
10.1371/journal.pone.0141840
10.3389/fnhum.2015.00059
10.1093/brain/awu132
10.1016/j.neuroimage.2007.02.012
10.1016/j.neuroimage.2013.03.023
10.1038/nn.3690
10.1006/nimg.2001.0978
10.1038/nrn2575
10.1016/j.neuroimage.2011.01.010
10.1109/TPAMI.2003.1195991
10.1523/JNEUROSCI.17-08-02859.1997
10.1371/journal.pcbi.0010042
10.1098/rsif.2014.0881
10.1016/j.neuroimage.2013.07.045
10.1016/j.neuroimage.2013.03.053
10.1038/30918
10.1214/aoms/1177728190
10.1016/j.neuroimage.2010.09.006
10.1093/cercor/bhl149
10.1016/j.conb.2014.10.014
10.1523/JNEUROSCI.3539-11.2011
10.1016/j.neuroimage.2009.10.003
10.1016/j.schres.2012.08.021
10.1016/j.neuroimage.2010.01.028
10.1371/journal.pbio.0060159
10.1371/journal.pone.0032766
10.1097/WCO.0b013e32833aa567
10.1006/nimg.1998.0384
10.1146/annurev-clinpsy-050212-185608
10.1016/j.neuron.2009.03.024
10.1093/cercor/bhg087
10.1016/j.neuron.2014.05.014
10.1371/journal.pone.0021976
10.1016/j.neuroimage.2007.07.007
10.1137/S003614450342480
10.1093/cercor/bhn003
10.1016/j.jneumeth.2014.09.003
10.1073/pnas.1003109107
10.1214/aoms/1177729694
10.1073/pnas.1001414107
10.1142/S0218195995000064
10.1523/JNEUROSCI.5062-08.2009
10.1126/science.1065103
10.1109/TPAMI.2006.120
ContentType Journal Article
Copyright 2016 The Authors. published by Wiley Periodicals, Inc.
2016. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: 2016 The Authors. published by Wiley Periodicals, Inc.
– notice: 2016. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID 24P
AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
3V.
7X7
7XB
88G
8FI
8FJ
8FK
8G5
ABUWG
AFKRA
AZQEC
BENPR
CCPQU
DWQXO
FYUFA
GHDGH
GNUQQ
GUQSH
K9.
M0S
M2M
M2O
MBDVC
PHGZM
PHGZT
PIMPY
PJZUB
PKEHL
PPXIY
PQEST
PQQKQ
PQUKI
PRINS
PSYQQ
Q9U
7X8
5PM
DOI 10.1002/brb3.448
DatabaseName Wiley-Blackwell Open Access Titles
CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
ProQuest Central (Corporate)
Health & Medical Collection
ProQuest Central (purchase pre-March 2016)
Psychology Database (Alumni)
Hospital Premium Collection
Hospital Premium Collection (Alumni Edition)
ProQuest Central (Alumni) (purchase pre-March 2016)
Research Library (Alumni Edition)
ProQuest Central (Alumni Edition)
ProQuest Central UK/Ireland
ProQuest Central Essentials
ProQuest Central
ProQuest One Community College
ProQuest Central Korea
Health Research Premium Collection
Health Research Premium Collection (Alumni)
ProQuest Central Student
Research Library Prep
ProQuest Health & Medical Complete (Alumni)
Health & Medical Collection (Alumni Edition)
Psychology Database
Research Library
Research Library (Corporate)
ProQuest Central Premium
ProQuest One Academic (New)
Publicly Available Content Database
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 Academic (retired)
ProQuest One Academic UKI Edition
ProQuest Central China
ProQuest One Psychology
ProQuest Central Basic
MEDLINE - Academic
PubMed Central (Full Participant titles)
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
Publicly Available Content Database
ProQuest One Psychology
Research Library Prep
ProQuest Central Student
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
ProQuest Health & Medical Complete (Alumni)
ProQuest Central (Alumni Edition)
ProQuest One Community College
Research Library (Alumni Edition)
ProQuest One Health & Nursing
ProQuest Central China
ProQuest Central
ProQuest Health & Medical Research Collection
Health Research Premium Collection
Health and Medicine Complete (Alumni Edition)
ProQuest Central Korea
ProQuest Research Library
ProQuest Central (New)
ProQuest Central Basic
ProQuest One Academic Eastern Edition
ProQuest Hospital Collection
Health Research Premium Collection (Alumni)
ProQuest Psychology Journals (Alumni)
ProQuest Hospital Collection (Alumni)
ProQuest Health & Medical Complete
ProQuest Psychology Journals
ProQuest One Academic UKI Edition
ProQuest One Academic
ProQuest One Academic (New)
ProQuest Central (Alumni)
MEDLINE - Academic
DatabaseTitleList MEDLINE - Academic
MEDLINE
Publicly Available Content Database
Database_xml – sequence: 1
  dbid: 24P
  name: Wiley Online Library Open Access
  url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html
  sourceTypes: Publisher
– sequence: 2
  dbid: NPM
  name: PubMed
  url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 3
  dbid: PIMPY
  name: Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Medicine
DocumentTitleAlternate H. Wang et al
EISSN 2162-3279
EndPage n/a
ExternalDocumentID PMC4782249
27088054
10_1002_brb3_448
BRB3448
Genre article
Research Support, Non-U.S. Gov't
Journal Article
GeographicLocations Beijing China
China
GeographicLocations_xml – name: China
– name: Beijing China
GrantInformation_xml – fundername: Zhejiang Provincial Natural Science Foundation of China
  funderid: LZ13C090001
– fundername: National Natural Science Foundation of China
  funderid: 81301284
– fundername: National Natural Science Foundation of China
  grantid: 81301284
– fundername: Zhejiang Provincial Natural Science Foundation of China
  grantid: LZ13C090001
GroupedDBID 0R~
1OC
24P
53G
5VS
7X7
8-0
8-1
8FI
8FJ
8G5
AAHHS
AAZKR
ABDBF
ABUWG
ACCFJ
ACCMX
ACGFO
ACUHS
ACXQS
ADBBV
ADKYN
ADRAZ
ADZMN
ADZOD
AEEZP
AENEX
AEQDE
AFKRA
AHMBA
AIAGR
AIWBW
AJBDE
ALAGY
ALIPV
ALMA_UNASSIGNED_HOLDINGS
ALUQN
AOIJS
AVUZU
AZQEC
BAWUL
BCNDV
BENPR
BPHCQ
BVXVI
CCPQU
D-8
D-9
DIK
DWQXO
EBS
EJD
ESX
FYUFA
GNUQQ
GODZA
GROUPED_DOAJ
GUQSH
GX1
HMCUK
HYE
IAO
IHR
ITC
KQ8
M2M
M2O
M48
M~E
OK1
PIMPY
PQQKQ
PROAC
PSYQQ
RNS
RPM
SUPJJ
TUS
UKHRP
WIN
AAMMB
AAYXX
AEFGJ
AFFHD
AGXDD
AIDQK
AIDYY
CITATION
PHGZM
PHGZT
CGR
CUY
CVF
ECM
EIF
NPM
3V.
7XB
8FK
K9.
MBDVC
PJZUB
PKEHL
PPXIY
PQEST
PQUKI
PRINS
Q9U
7X8
5PM
ID FETCH-LOGICAL-c5048-a89c540663e4a28ffd622a2f76d0fa1a38eedd0503ece62d694487f1fe55615f3
IEDL.DBID BENPR
ISICitedReferencesCount 154
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000374761900003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 2162-3279
IngestDate Tue Nov 04 01:58:39 EST 2025
Sun Nov 09 09:00:47 EST 2025
Sat Nov 29 14:55:27 EST 2025
Mon Jul 21 06:03:53 EDT 2025
Tue Nov 18 20:12:32 EST 2025
Sat Nov 29 03:27:19 EST 2025
Wed Jan 22 16:34:42 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 4
Keywords gray matter volume
structural MRI
hub
Brain network
reliability
Language English
License Attribution
http://creativecommons.org/licenses/by/4.0
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c5048-a89c540663e4a28ffd622a2f76d0fa1a38eedd0503ece62d694487f1fe55615f3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
OpenAccessLink https://www.proquest.com/docview/2288591347?pq-origsite=%requestingapplication%
PMID 27088054
PQID 2288591347
PQPubID 976341
PageCount 21
ParticipantIDs pubmedcentral_primary_oai_pubmedcentral_nih_gov_4782249
proquest_miscellaneous_1785746339
proquest_journals_2288591347
pubmed_primary_27088054
crossref_citationtrail_10_1002_brb3_448
crossref_primary_10_1002_brb3_448
wiley_primary_10_1002_brb3_448_BRB3448
PublicationCentury 2000
PublicationDate April 2016
PublicationDateYYYYMMDD 2016-04-01
PublicationDate_xml – month: 04
  year: 2016
  text: April 2016
PublicationDecade 2010
PublicationPlace United States
PublicationPlace_xml – name: United States
– name: Los Angeles
– name: Hoboken
PublicationTitle Brain and behavior
PublicationTitleAlternate Brain Behav
PublicationYear 2016
Publisher John Wiley & Sons, Inc
John Wiley and Sons Inc
Publisher_xml – name: John Wiley & Sons, Inc
– name: John Wiley and Sons Inc
References 2009; 44
2002; 15
2006; 31
2010; 107
2013a; 14
2005; 210
2015; 31
2004; 8
1995; 34
2008; 39
2000; 50
1951; 22
2011; 54
2011; 56
2008; 6
2012; 16
1962; 33
2013; 7
2012; 13
2015b; 21
1998; 393
2014; 137
2007; 36
2013; 9
2007; 38
2010; 23
2013; 17
2009; 10
2013; 10
1997; 385
2006; 28
2015a; 9
2006; 26
2008; 28
2014; 15
1997; 17
2007; 2
2014; 17
2009; 19
2010; 4
2012; 22
2003; 45
2007; 17
2015; 12
2015; 56
2014; 237
2012; 141
2010; 38
2009; 62
2002; 296
2013b; 33
2008; 18
2009a; 132
2013; 83
2015; 10
2011; 31
2002
2015; 9
2014; 83
2011; 6
1995; 3
2012; 33
1995; 5
2011; 7
2009; 29
2014; 86
1999; 9
2015; 25
2010; 49
2009; 30
2009b; 4
2004; 14
2013; 80
2003; 25
1956; 27
2005; 1
2005; 15
1979; 86
2012; 7
2010; 52
2010; 50
1998; 8
e_1_2_9_75_1
e_1_2_9_31_1
e_1_2_9_52_1
e_1_2_9_50_1
e_1_2_9_73_1
e_1_2_9_79_1
e_1_2_9_94_1
e_1_2_9_10_1
e_1_2_9_35_1
e_1_2_9_56_1
e_1_2_9_77_1
e_1_2_9_12_1
e_1_2_9_33_1
e_1_2_9_54_1
e_1_2_9_90_1
e_1_2_9_92_1
e_1_2_9_71_1
e_1_2_9_14_1
e_1_2_9_39_1
e_1_2_9_16_1
e_1_2_9_58_1
e_1_2_9_18_1
Fornito A. (e_1_2_9_37_1) 2010; 4
e_1_2_9_41_1
e_1_2_9_64_1
e_1_2_9_87_1
e_1_2_9_20_1
e_1_2_9_62_1
e_1_2_9_89_1
e_1_2_9_45_1
e_1_2_9_68_1
e_1_2_9_83_1
e_1_2_9_24_1
e_1_2_9_43_1
e_1_2_9_66_1
e_1_2_9_85_1
e_1_2_9_8_1
e_1_2_9_6_1
e_1_2_9_81_1
e_1_2_9_4_1
e_1_2_9_60_1
e_1_2_9_2_1
e_1_2_9_26_1
e_1_2_9_49_1
e_1_2_9_28_1
e_1_2_9_47_1
Burnham K. P. (e_1_2_9_22_1) 2002
e_1_2_9_30_1
e_1_2_9_53_1
e_1_2_9_74_1
e_1_2_9_51_1
e_1_2_9_72_1
e_1_2_9_11_1
e_1_2_9_34_1
e_1_2_9_57_1
e_1_2_9_78_1
e_1_2_9_13_1
e_1_2_9_32_1
e_1_2_9_55_1
e_1_2_9_76_1
e_1_2_9_91_1
e_1_2_9_93_1
e_1_2_9_70_1
e_1_2_9_15_1
e_1_2_9_38_1
e_1_2_9_17_1
e_1_2_9_36_1
e_1_2_9_59_1
e_1_2_9_19_1
Wang J. (e_1_2_9_84_1) 2015; 9
e_1_2_9_42_1
e_1_2_9_63_1
e_1_2_9_88_1
Velazquez J. L. P. (e_1_2_9_80_1) 2013; 7
e_1_2_9_40_1
e_1_2_9_61_1
e_1_2_9_21_1
e_1_2_9_46_1
e_1_2_9_67_1
e_1_2_9_23_1
e_1_2_9_44_1
e_1_2_9_65_1
e_1_2_9_86_1
e_1_2_9_7_1
e_1_2_9_5_1
e_1_2_9_82_1
e_1_2_9_3_1
e_1_2_9_9_1
e_1_2_9_25_1
e_1_2_9_27_1
e_1_2_9_48_1
e_1_2_9_69_1
e_1_2_9_29_1
References_xml – volume: 137
  start-page: 2382
  year: 2014
  end-page: 2395
  article-title: The hubs of the human connectome are generally implicated in the anatomy of brain disorders
  publication-title: Brain
– volume: 7
  start-page: e32766
  year: 2012
  article-title: Effects of different correlation metrics and preprocessing factors on small‐world brain functional networks: a resting‐state functional MRI study
  publication-title: PLoS One
– volume: 50
  start-page: 1
  year: 2000
  end-page: 18
  article-title: The mahalanobis distance
  publication-title: Chemometr. Intell. Lab. Syst.
– volume: 80
  start-page: 53
  year: 2013
  end-page: 61
  article-title: The human connectome: origins and challenges
  publication-title: NeuroImage
– volume: 33
  start-page: 2889
  year: 2013b
  end-page: 2899
  article-title: The convergence of maturational change and structural covariance in human cortical networks
  publication-title: J. Neurosci.
– volume: 83
  start-page: 238
  year: 2014
  end-page: 251
  article-title: Intrinsic and task‐evoked network architectures of the human brain
  publication-title: Neuron
– volume: 54
  start-page: 1262
  year: 2011
  end-page: 1279
  article-title: Conserved and variable architecture of human white matter connectivity
  publication-title: NeuroImage
– volume: 7
  start-page: 113
  year: 2011
  end-page: 140
  article-title: Brain graphs: graphical models of the human brain connectome
  publication-title: Annu. Rev. Clin. Psychol.
– volume: 17
  start-page: 652
  year: 2014
  end-page: 660
  article-title: Contributions and challenges for network models in cognitive neuroscience
  publication-title: Nat. Neurosci.
– volume: 17
  start-page: 603
  year: 2013
  end-page: 605
  article-title: Brain network interactions in health and disease
  publication-title: Trends Cogn. Sci.
– volume: 86
  start-page: 231
  year: 2014
  end-page: 243
  article-title: Test–retest reliability of structural brain networks from diffusion MRI
  publication-title: NeuroImage
– volume: 15
  start-page: 683
  year: 2014
  end-page: 695
  article-title: Modern network science of neurological disorders
  publication-title: Nat. Rev. Neurosci.
– volume: 45
  start-page: 167
  year: 2003
  end-page: 256
  article-title: The structure and function of complex networks
  publication-title: SIAM Rev.
– volume: 17
  start-page: 2407
  year: 2007
  end-page: 2419
  article-title: Small‐world anatomical networks in the human brain revealed by cortical thickness from MRI
  publication-title: Cereb. Cortex
– volume: 21
  start-page: 290
  year: 2015b
  end-page: 305
  article-title: Understanding structural‐functional relationships in the human brain: a large‐scale network perspective
  publication-title: Neuroscientist
– volume: 6
  start-page: e159
  year: 2008
  article-title: Mapping the structural core of human cerebral cortex
  publication-title: PLoS Biol.
– volume: 28
  start-page: 917
  year: 2006
  end-page: 929
  article-title: From sample similarity to ensemble similarity: probabilistic distance measures in reproducing kernel Hilbert space
  publication-title: IEEE Trans. Pattern Anal. Mach. Intell.
– volume: 3
  start-page: 190
  year: 1995
  end-page: 208
  article-title: Automatic 3‐D model‐based neuroanatomical segmentation
  publication-title: Hum. Brain Mapp.
– volume: 4
  start-page: e5226
  year: 2009b
  article-title: Uncovering intrinsic modular organization of spontaneous brain activity in humans
  publication-title: PLoS One
– volume: 10
  start-page: 186
  year: 2009
  end-page: 198
  article-title: Complex brain networks: graph theoretical analysis of structural and functional systems
  publication-title: Nat. Rev. Neurosci.
– volume: 54
  start-page: 1862
  year: 2011
  end-page: 1871
  article-title: Brain anatomical networks in early human brain development
  publication-title: NeuroImage
– volume: 16
  start-page: 181
  year: 2012
  end-page: 188
  article-title: Characterizing variation in the functional connectome: promise and pitfalls
  publication-title: Trends Cogn Sci.
– volume: 237
  start-page: 103
  year: 2014
  end-page: 107
  article-title: Measuring individual morphological relationship of cortical regions
  publication-title: J. Neurosci. Methods
– volume: 107
  start-page: 18191
  year: 2010
  end-page: 18196
  article-title: Network‐level structural covariance in the developing brain
  publication-title: Proc. Natl Acad. Sci. USA
– volume: 49
  start-page: 3132
  year: 2010
  end-page: 3148
  article-title: Identifying the brain's most globally connected regions
  publication-title: NeuroImage
– volume: 52
  start-page: 1302
  year: 2010
  end-page: 1313
  article-title: Network‐level analysis of cortical thickness of the epileptic brain
  publication-title: NeuroImage
– volume: 15
  start-page: 273
  year: 2002
  end-page: 289
  article-title: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single‐subject brain
  publication-title: NeuroImage
– volume: 38
  start-page: 95
  year: 2007
  end-page: 113
  article-title: A fast diffeomorphic image registration algorithm
  publication-title: NeuroImage
– volume: 17
  start-page: 683
  year: 2013
  end-page: 696
  article-title: Network hubs in the human brain
  publication-title: Trends Cogn. Sci.
– volume: 80
  start-page: 489
  year: 2013
  end-page: 504
  article-title: Networks of anatomical covariance
  publication-title: NeuroImage
– volume: 19
  start-page: 524
  year: 2009
  end-page: 536
  article-title: Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography
  publication-title: Cereb. Cortex
– volume: 80
  start-page: 397
  year: 2013
  end-page: 404
  article-title: The parcellation‐based connectome: limitations and extensions
  publication-title: NeuroImage
– volume: 9
  start-page: 386
  year: 2015a
  article-title: GRETNA: a graph theoretical network analysis toolbox for imaging connectomics
  publication-title: Front. Hum. Neurosci.
– volume: 39
  start-page: 1064
  year: 2008
  end-page: 1080
  article-title: Construction of a 3D probabilistic atlas of human cortical structures
  publication-title: NeuroImage
– volume: 50
  start-page: 1497
  year: 2010
  end-page: 1510
  article-title: Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks
  publication-title: NeuroImage
– volume: 18
  start-page: 2374
  year: 2008
  end-page: 2381
  article-title: Revealing modular architecture of human brain structural networks by using cortical thickness from MRI
  publication-title: Cereb. Cortex
– volume: 56
  start-page: 299
  year: 2015
  end-page: 320
  article-title: Annual research review: growth connectomics–the organization and reorganization of brain networks during normal and abnormal development
  publication-title: J. Child Psychol. Psychiatry
– volume: 210
  start-page: 411
  year: 2005
  end-page: 417
  article-title: Developmental mechanics of the primate cerebral cortex
  publication-title: Anat. Embryol. (Berl)
– volume: 30
  start-page: 1511
  year: 2009
  end-page: 1523
  article-title: Parcellation‐dependent small‐world brain functional networks: a resting‐state fMRI study
  publication-title: Hum. Brain Mapp.
– volume: 141
  start-page: 109
  year: 2012
  end-page: 118
  article-title: Abnormal topological organization of structural brain networks in schizophrenia
  publication-title: Schizophr. Res.
– volume: 15
  start-page: 1332
  year: 2005
  end-page: 1342
  article-title: Neurophysiological architecture of functional magnetic resonance images of human brain
  publication-title: Cereb. Cortex
– volume: 296
  start-page: 910
  year: 2002
  end-page: 913
  article-title: Specificity and stability in topology of protein networks
  publication-title: Science
– volume: 8
  start-page: 418
  year: 2004
  end-page: 425
  article-title: Organization, development and function of complex brain networks
  publication-title: Trends Cogn. Sci.
– volume: 6
  start-page: e21935
  year: 2011
  article-title: Hierarchical anatomical brain networks for MCI prediction: revisiting volumetric measures
  publication-title: PLoS One
– volume: 31
  start-page: 164
  year: 2015
  end-page: 172
  article-title: The heavy tail of the human brain
  publication-title: Curr. Opin. Neurobiol.
– volume: 83
  start-page: 901
  year: 2013
  end-page: 911
  article-title: Normalization of similarity‐based individual brain networks from gray matter MRI and its association with neurodevelopment in infants with intrauterine growth restriction
  publication-title: NeuroImage
– volume: 31
  start-page: 968
  year: 2006
  end-page: 980
  article-title: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest
  publication-title: NeuroImage
– volume: 107
  start-page: 9885
  year: 2010
  end-page: 9890
  article-title: Functional connectivity density mapping
  publication-title: Proc. Natl Acad. Sci. USA
– volume: 28
  start-page: 9239
  year: 2008
  end-page: 9248
  article-title: Hierarchical organization of human cortical networks in health and schizophrenia
  publication-title: J. Neurosci.
– volume: 44
  start-page: 715
  year: 2009
  end-page: 723
  article-title: Age‐related changes in modular organization of human brain functional networks
  publication-title: NeuroImage
– volume: 33
  start-page: 1065
  year: 1962
  end-page: 1076
  article-title: On estimation of a probability density function and mode
  publication-title: Ann. Math. Stat.
– volume: 52
  start-page: 1059
  year: 2010
  end-page: 1069
  article-title: Complex network measures of brain connectivity: uses and interpretations
  publication-title: NeuroImage
– volume: 62
  start-page: 42
  year: 2009
  end-page: 52
  article-title: Neurodegenerative diseases target large‐scale human brain networks
  publication-title: Neuron
– volume: 5
  start-page: 75
  year: 1995
  end-page: 91
  article-title: Computing the Fréchet distance between two polygonal curves
  publication-title: Int. J. Comput. Geom. Appl.
– volume: 38
  start-page: 2916
  year: 2010
  end-page: 2957
  article-title: Kernel density estimation via diffusion
  publication-title: Ann. Stat.
– volume: 86
  start-page: 420
  year: 1979
  end-page: 428
  article-title: Intraclass correlations: uses in assessing rater reliability
  publication-title: Psychol. Bull.
– volume: 9
  start-page: 59
  year: 2015
  article-title: Test‐retest reliability of white matter structural brain networks: a multiband diffusion MRI study
  publication-title: Front. Hum. Neurosci.
– volume: 9
  start-page: 18
  year: 1999
  end-page: 45
  article-title: MRI‐Based topographic parcellation of human cerebral white matter and nuclei II. Rationale and applications with systematics of cerebral connectivity
  publication-title: NeuroImage
– volume: 33
  start-page: 899
  year: 2012
  end-page: 913
  article-title: Changing topological patterns in normal aging using large‐scale structural networks
  publication-title: Neurobiol. Aging
– volume: 29
  start-page: 1860
  year: 2009
  end-page: 1873
  article-title: Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease
  publication-title: J. Neurosci.
– volume: 33
  start-page: 552
  year: 2012
  end-page: 568
  article-title: Age‐related changes in topological organization of structural brain networks in healthy individuals
  publication-title: Hum. Brain Mapp.
– volume: 34
  start-page: 537
  year: 1995
  end-page: 541
  article-title: Functional connectivity in the motor cortex of resting human brain using echo‐planar MRI
  publication-title: Magn. Reson. Med.
– volume: 50
  start-page: 970
  year: 2010
  end-page: 983
  article-title: Whole‐brain anatomical networks: does the choice of nodes matter?
  publication-title: NeuroImage
– volume: 385
  start-page: 313
  year: 1997
  end-page: 318
  article-title: A tension‐based theory of morphogenesis and compact wiring in the central nervous system
  publication-title: Nature
– volume: 132
  start-page: 3366
  year: 2009a
  end-page: 3379
  article-title: Impaired small‐world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load
  publication-title: Brain
– volume: 1
  start-page: e42
  year: 2005
  article-title: The human connectome: a structural description of the human brain
  publication-title: PLoS Comput. Biol.
– volume: 2
  start-page: e597
  year: 2007
  article-title: Mapping human whole‐brain structural networks with diffusion MRI
  publication-title: PLoS One
– volume: 25
  start-page: 564
  year: 2003
  end-page: 577
  article-title: Kernel‐based object tracking
  publication-title: IEEE Trans. Pattern. Anal. Mach. Intell.
– volume: 6
  start-page: e21976
  year: 2011
  article-title: Graph theoretical analysis of functional brain networks: test‐retest evaluation on short‐ and long‐term resting‐state functional MRI data
  publication-title: PLoS One
– volume: 23
  start-page: 341
  year: 2010
  end-page: 350
  article-title: Graph theoretical modeling of brain connectivity
  publication-title: Curr. Opin. Neurol.
– volume: 56
  start-page: 235
  year: 2011
  end-page: 245
  article-title: Age‐related alterations in the modular organization of structural cortical network by using cortical thickness from MRI
  publication-title: NeuroImage
– volume: 12
  start-page: 20140881
  year: 2015
  article-title: Network morphospace
  publication-title: J. R. Soc. Interface
– volume: 27
  start-page: 832
  year: 1956
  end-page: 837
  article-title: Remarks on some nonparametric estimates of a density function
  publication-title: Ann. Math. Stat.
– volume: 10
  start-page: 524
  year: 2013
  end-page: 539
  article-title: Imaging human connectomes at the macroscale
  publication-title: Nat. Methods
– volume: 22
  start-page: 79
  year: 1951
  end-page: 86
  article-title: On information and sufficiency
  publication-title: Ann. Math. Stat.
– volume: 28
  start-page: 4756
  year: 2008
  end-page: 4766
  article-title: Structural insights into aberrant topological patterns of large‐scale cortical networks in Alzheimer's disease
  publication-title: J. Neurosci.
– volume: 14
  start-page: 11
  year: 2004
  end-page: 22
  article-title: Automatically parcellating the human cerebral cortex
  publication-title: Cereb. Cortex
– volume: 8
  start-page: 372
  year: 1998
  end-page: 384
  article-title: Gyri of the human neocortex: an MRI‐based analysis of volume and variance
  publication-title: Cereb. Cortex
– volume: 17
  start-page: 2859
  year: 1997
  end-page: 2868
  article-title: Correlated size variations in human visual cortex, lateral geniculate nucleus, and optic tract
  publication-title: J. Neurosci.
– volume: 31
  start-page: 15775
  year: 2011
  end-page: 15786
  article-title: Rich‐club organization of the human connectome
  publication-title: J. Neurosci.
– volume: 36
  start-page: 645
  year: 2007
  end-page: 660
  article-title: Characterizing brain anatomical connections using diffusion weighted MRI and graph theory
  publication-title: NeuroImage
– volume: 10
  start-page: e0141840
  year: 2015
  article-title: Mapping individual brain networks using statistical similarity in regional morphology from MRI
  publication-title: PLoS One
– volume: 107
  start-page: 4734
  year: 2010
  end-page: 4739
  article-title: Toward discovery science of human brain function
  publication-title: Proc. Natl Acad. Sci. USA
– volume: 13
  start-page: 336
  year: 2012
  end-page: 349
  article-title: The economy of brain network organization
  publication-title: Nat. Rev. Neurosci.
– volume: 14
  start-page: 322
  year: 2013a
  end-page: 336
  article-title: Imaging structural co‐variance between human brain regions
  publication-title: Nat. Rev. Neurosci.
– year: 2002
– volume: 4
  start-page: 22
  year: 2010
  article-title: Network scaling effects in graph analytic studies of human resting‐state FMRI data
  publication-title: Front. Syst. Neurosci.
– volume: 7
  start-page: 37
  year: 2013
  article-title: Information gain in the brain's resting state: a new perspective on autism
  publication-title: Front. Neuroinform.
– volume: 9
  start-page: 91
  year: 2013
  end-page: 121
  article-title: Network analysis: an integrative approach to the structure of psychopathology
  publication-title: Annu. Rev. Clin. Psychol.
– volume: 26
  start-page: 63
  year: 2006
  end-page: 72
  article-title: A resilient, low‐frequency, small‐world human brain functional network with highly connected association cortical hubs
  publication-title: J. Neurosci.
– volume: 25
  start-page: 733
  year: 2015
  end-page: 748
  article-title: Connectomics: a new paradigm for understanding brain disease
  publication-title: Eur. Neuropsychopharmacol.
– volume: 22
  start-page: 1530
  year: 2012
  end-page: 1541
  article-title: Similarity‐based extraction of individual networks from gray matter MRI scans
  publication-title: Cereb. Cortex
– volume: 393
  start-page: 440
  year: 1998
  end-page: 442
  article-title: Collective dynamics of ‘small‐world'networks
  publication-title: Nature
– ident: e_1_2_9_69_1
  doi: 10.1016/j.neuroimage.2007.09.031
– ident: e_1_2_9_44_1
  doi: 10.1093/brain/awp089
– ident: e_1_2_9_47_1
  doi: 10.1016/j.tics.2013.09.012
– ident: e_1_2_9_58_1
  doi: 10.1016/j.neuroimage.2008.09.062
– ident: e_1_2_9_70_1
  doi: 10.1037/0033-2909.86.2.420
– ident: e_1_2_9_9_1
  doi: 10.1016/j.tics.2013.09.004
– ident: e_1_2_9_27_1
  doi: 10.1002/hbm.460030304
– ident: e_1_2_9_19_1
  doi: 10.1146/annurev-clinpsy-040510-143934
– volume: 9
  start-page: 386
  year: 2015
  ident: e_1_2_9_84_1
  article-title: GRETNA: a graph theoretical network analysis toolbox for imaging connectomics
  publication-title: Front. Hum. Neurosci.
– ident: e_1_2_9_10_1
  doi: 10.1523/JNEUROSCI.1929-08.2008
– ident: e_1_2_9_60_1
  doi: 10.1214/aoms/1177704472
– volume: 4
  start-page: 22
  year: 2010
  ident: e_1_2_9_37_1
  article-title: Network scaling effects in graph analytic studies of human resting‐state FMRI data
  publication-title: Front. Syst. Neurosci.
– ident: e_1_2_9_48_1
  doi: 10.1007/s00429-005-0041-5
– ident: e_1_2_9_33_1
  doi: 10.1016/j.neuroimage.2013.05.054
– ident: e_1_2_9_51_1
  doi: 10.1093/cercor/8.4.372
– ident: e_1_2_9_75_1
  doi: 10.1038/nrn3801
– ident: e_1_2_9_3_1
  doi: 10.1038/nrn3465
– ident: e_1_2_9_14_1
  doi: 10.1073/pnas.0911855107
– ident: e_1_2_9_93_1
  doi: 10.1016/j.neurobiolaging.2010.06.022
– ident: e_1_2_9_2_1
  doi: 10.1523/JNEUROSCI.3874-05.2006
– ident: e_1_2_9_31_1
  doi: 10.1016/S0169-7439(99)00047-7
– ident: e_1_2_9_79_1
  doi: 10.1038/385313a0
– ident: e_1_2_9_32_1
  doi: 10.1016/j.neuroimage.2006.01.021
– ident: e_1_2_9_81_1
  doi: 10.1111/jcpp.12365
– ident: e_1_2_9_4_1
  doi: 10.1523/JNEUROSCI.3554-12.2013
– ident: e_1_2_9_85_1
  doi: 10.1177/1073858414537560
– ident: e_1_2_9_92_1
  doi: 10.1371/journal.pone.0021935
– ident: e_1_2_9_43_1
  doi: 10.1523/JNEUROSCI.0141-08.2008
– ident: e_1_2_9_50_1
  doi: 10.1016/j.tics.2012.02.001
– ident: e_1_2_9_45_1
  doi: 10.1371/journal.pone.0005226
– ident: e_1_2_9_25_1
  doi: 10.1016/j.neuroimage.2009.11.001
– ident: e_1_2_9_13_1
  doi: 10.1002/mrm.1910340409
– ident: e_1_2_9_29_1
  doi: 10.1038/nmeth.2482
– ident: e_1_2_9_36_1
  doi: 10.1016/j.euroneuro.2014.02.011
– volume-title: Model selection and multimodel inference: a practical information‐theoretic approach
  year: 2002
  ident: e_1_2_9_22_1
– ident: e_1_2_9_61_1
  doi: 10.1016/j.neuroimage.2010.05.045
– ident: e_1_2_9_17_1
  doi: 10.1016/j.neuroimage.2013.09.054
– ident: e_1_2_9_21_1
  doi: 10.1038/nrn3214
– ident: e_1_2_9_34_1
  doi: 10.1016/j.neuroimage.2010.07.025
– ident: e_1_2_9_88_1
  doi: 10.1016/j.neuroimage.2009.12.027
– ident: e_1_2_9_39_1
  doi: 10.1371/journal.pone.0000597
– ident: e_1_2_9_87_1
  doi: 10.1002/hbm.21232
– ident: e_1_2_9_38_1
  doi: 10.1093/cercor/bhn102
– ident: e_1_2_9_82_1
  doi: 10.1002/hbm.20623
– ident: e_1_2_9_16_1
  doi: 10.1214/10-AOS799
– ident: e_1_2_9_73_1
  doi: 10.1016/j.tics.2004.07.008
– ident: e_1_2_9_66_1
  doi: 10.1093/cercor/bhi016
– ident: e_1_2_9_76_1
  doi: 10.1093/cercor/bhr221
– ident: e_1_2_9_53_1
  doi: 10.1371/journal.pone.0141840
– ident: e_1_2_9_90_1
  doi: 10.3389/fnhum.2015.00059
– ident: e_1_2_9_30_1
  doi: 10.1093/brain/awu132
– ident: e_1_2_9_49_1
  doi: 10.1016/j.neuroimage.2007.02.012
– ident: e_1_2_9_71_1
  doi: 10.1016/j.neuroimage.2013.03.023
– ident: e_1_2_9_72_1
  doi: 10.1038/nn.3690
– ident: e_1_2_9_78_1
  doi: 10.1006/nimg.2001.0978
– ident: e_1_2_9_20_1
  doi: 10.1038/nrn2575
– ident: e_1_2_9_24_1
  doi: 10.1016/j.neuroimage.2011.01.010
– ident: e_1_2_9_28_1
  doi: 10.1109/TPAMI.2003.1195991
– ident: e_1_2_9_6_1
  doi: 10.1523/JNEUROSCI.17-08-02859.1997
– ident: e_1_2_9_74_1
  doi: 10.1371/journal.pcbi.0010042
– ident: e_1_2_9_8_1
  doi: 10.1098/rsif.2014.0881
– ident: e_1_2_9_12_1
  doi: 10.1016/j.neuroimage.2013.07.045
– ident: e_1_2_9_62_1
  doi: 10.1016/j.neuroimage.2013.03.053
– ident: e_1_2_9_86_1
  doi: 10.1038/30918
– ident: e_1_2_9_64_1
  doi: 10.1214/aoms/1177728190
– ident: e_1_2_9_11_1
  doi: 10.1016/j.neuroimage.2010.09.006
– ident: e_1_2_9_42_1
  doi: 10.1093/cercor/bhl149
– ident: e_1_2_9_63_1
  doi: 10.1016/j.conb.2014.10.014
– ident: e_1_2_9_46_1
  doi: 10.1523/JNEUROSCI.3539-11.2011
– volume: 7
  start-page: 37
  year: 2013
  ident: e_1_2_9_80_1
  article-title: Information gain in the brain's resting state: a new perspective on autism
  publication-title: Front. Neuroinform.
– ident: e_1_2_9_65_1
  doi: 10.1016/j.neuroimage.2009.10.003
– ident: e_1_2_9_89_1
  doi: 10.1016/j.schres.2012.08.021
– ident: e_1_2_9_67_1
  doi: 10.1016/j.neuroimage.2010.01.028
– ident: e_1_2_9_40_1
  doi: 10.1371/journal.pbio.0060159
– ident: e_1_2_9_55_1
  doi: 10.1371/journal.pone.0032766
– ident: e_1_2_9_41_1
  doi: 10.1097/WCO.0b013e32833aa567
– ident: e_1_2_9_56_1
  doi: 10.1006/nimg.1998.0384
– ident: e_1_2_9_15_1
  doi: 10.1146/annurev-clinpsy-050212-185608
– ident: e_1_2_9_68_1
  doi: 10.1016/j.neuron.2009.03.024
– ident: e_1_2_9_35_1
  doi: 10.1093/cercor/bhg087
– ident: e_1_2_9_26_1
  doi: 10.1016/j.neuron.2014.05.014
– ident: e_1_2_9_83_1
  doi: 10.1371/journal.pone.0021976
– ident: e_1_2_9_7_1
  doi: 10.1016/j.neuroimage.2007.07.007
– ident: e_1_2_9_59_1
  doi: 10.1137/S003614450342480
– ident: e_1_2_9_23_1
  doi: 10.1093/cercor/bhn003
– ident: e_1_2_9_52_1
  doi: 10.1016/j.jneumeth.2014.09.003
– ident: e_1_2_9_94_1
  doi: 10.1073/pnas.1003109107
– ident: e_1_2_9_54_1
  doi: 10.1214/aoms/1177729694
– ident: e_1_2_9_77_1
  doi: 10.1073/pnas.1001414107
– ident: e_1_2_9_5_1
  doi: 10.1142/S0218195995000064
– ident: e_1_2_9_18_1
  doi: 10.1523/JNEUROSCI.5062-08.2009
– ident: e_1_2_9_57_1
  doi: 10.1126/science.1065103
– ident: e_1_2_9_91_1
  doi: 10.1109/TPAMI.2006.120
SSID ssj0000514240
Score 2.4535046
Snippet Introduction Structural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local...
Structural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological features...
IntroductionStructural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological...
SourceID pubmedcentral
proquest
pubmed
crossref
wiley
SourceType Open Access Repository
Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage e00448
SubjectTerms Adult
Brain - anatomy & histology
Brain - diagnostic imaging
Brain network
Brain research
Female
Gray Matter - anatomy & histology
Gray Matter - diagnostic imaging
gray matter volume
hub
Humans
Labeling
Magnetic Resonance Imaging - methods
Magnetic Resonance Imaging - standards
Male
Medical imaging
Morphology
Nerve Net - anatomy & histology
Nerve Net - diagnostic imaging
Original Research
Probability distribution
reliability
Reproducibility of Results
structural MRI
Studies
Young Adult
SummonAdditionalLinks – databaseName: Wiley-Blackwell Open Access Titles
  dbid: 24P
  link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3LbtUwELVoQYgNb-iFgoyEYEPU-HFjmx1FVGyoKh5Sd9HEcdpKbS5Kciuxop-AxB_2S5iJk8BVQULqygs_Yscz9vF4fIax57oA4YxSiXaFSbTxkLg0LZMU_Ny4MpUh832wCbO7a_f33d7gVUlvYSI_xGRwI83o12tScCjard-koUWDx0A8XKyxq0IoS2EbpN6b7CvE6y3795BSZDJR0riRezaVW2Pl1d3oAsS86Cn5J4Ltt6CdW5fp_G12cwCe_E2UlDvsSqjvsusfhqv1e-z7J9zEjsP52Y92WZBxhp8scA7GtZEXFEuC19FrvH3NPXnI-Bh7gp8A0TwcvOJdjLnQ1_ATF3R86smhLjki2-787GdD9t6ONziMSBT-7T77svPu89v3yRCdIfFzVPsErPMI9xCxBA3SVlWZSQmyMlmZViBAWdx-S6KbCT5ksswcjtdUogoUkXNeqQdsvV7UYYNxq2WJC4NRIaTaA1iReigwAXBOizBjL8dZyv1AXU4RNI7zSLosc_qfObY_Y8-mkl8jXcdfymyOE50PCtvmUlpi8lPaYBNTNqoa3Z9AHRbLNhfGzo3OlHIz9jDKxfQRaXC5Rvg7Y2ZFYqYCROO9mlMfHfZ03ppAmsY2X_QS889-59sftxWmj_634GN2A-FdFv2MNtl61yzDE3bNn3ZHbfO0V5dfZtseZg
  priority: 102
  providerName: Wiley-Blackwell
Title Single‐subject morphological brain networks: connectivity mapping, topological characterization and test–retest reliability
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fbrb3.448
https://www.ncbi.nlm.nih.gov/pubmed/27088054
https://www.proquest.com/docview/2288591347
https://www.proquest.com/docview/1785746339
https://pubmed.ncbi.nlm.nih.gov/PMC4782249
Volume 6
WOSCitedRecordID wos000374761900003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAON
  databaseName: DOAJ Directory of Open Access Journals
  customDbUrl:
  eissn: 2162-3279
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000514240
  issn: 2162-3279
  databaseCode: DOA
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: https://www.doaj.org/
  providerName: Directory of Open Access Journals
– providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 2162-3279
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000514240
  issn: 2162-3279
  databaseCode: M~E
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
– providerCode: PRVPQU
  databaseName: Health & Medical Collection
  customDbUrl:
  eissn: 2162-3279
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000514240
  issn: 2162-3279
  databaseCode: 7X7
  dateStart: 20110901
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/healthcomplete
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central
  customDbUrl:
  eissn: 2162-3279
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000514240
  issn: 2162-3279
  databaseCode: BENPR
  dateStart: 20110901
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Psychology Database
  customDbUrl:
  eissn: 2162-3279
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000514240
  issn: 2162-3279
  databaseCode: M2M
  dateStart: 20110901
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/psychology
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Publicly Available Content Database
  customDbUrl:
  eissn: 2162-3279
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000514240
  issn: 2162-3279
  databaseCode: PIMPY
  dateStart: 20110901
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Research Library
  customDbUrl:
  eissn: 2162-3279
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000514240
  issn: 2162-3279
  databaseCode: M2O
  dateStart: 20110901
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/pqrl
  providerName: ProQuest
– providerCode: PRVWIB
  databaseName: Wiley Online Library Free Content
  customDbUrl:
  eissn: 2162-3279
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000514240
  issn: 2162-3279
  databaseCode: WIN
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: https://onlinelibrary.wiley.com
  providerName: Wiley-Blackwell
– providerCode: PRVWIB
  databaseName: Wiley Online Library Open Access
  customDbUrl:
  eissn: 2162-3279
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000514240
  issn: 2162-3279
  databaseCode: 24P
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html
  providerName: Wiley-Blackwell
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3Pb9MwFLZYh9Au_N4ojMpICC5ESxw3jrkgijaxQ0s0QJRT5DgOVOrSkaRInNifgMR_uL-E92InbBpw4eKosutaffZ7X59fv4-QxzxTgRRh6HGZCY8LrTzp-7nnKz0WMveZiXQrNiFms3g-l4lLuNWurLLzia2jzlcac-R7jMVItRZy8eLki4eqUXi76iQ0NsgmMpXxAdmc7M-Soz7LguzeELM61lmf7WUV_HrkKPdzPg5dApeXayTPY9c2-Bzc-N9l3yTXHeykL-0-uUWumPI2uTZ1F-t3yPe3EMKW5uz0R73OMDVDj1dggc4z0gyVJGhpa8br51RjfYy2yhP0WCHJw6dntLGKC-07dM8Ebf_oSVWZU8C1zdnpzwqzvQ2tzHJhacK_3SXvD_bfvXrtOW0GT4_h0HsqlhrAHuAVwxWLiyKPGFOsEFHuFypQYQzBN0eyGaNNxPJIwncuiqAwqMc5LsJtMihXpblHaMxZDm5BhMb4XCsVB75WGTyUkpIHZkiedpZKtSMuR_2MZWopl1mKNk1h_iF51I88sWQdfxiz2xkqdce1Tn9bCabou-Gg4e2JKs1qXaeBiMeCR2Eoh2TH7o3-Q5gAZw3gd0jEhV3TD0AS74s95eJzS-bNEaJxmPNJu7_-uu50cjQJ4Xn_3-t_QLYA0kW2tmiXDJpqbR6Sq_prs6irEdlgPIFWzEXbxiN3YEZtLgLaKZu27RvoSQ6nyUd49eFw9gsjmino
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NbtQwEB6VgoAL_z8LBYzEz4WoieONYySEKFC1aneFoEi9BcdxYKVttiRZUE_0EZB4Dx6qT8JMnIRWBW49cMrBjuMkn2fG9vj7AB6IVAdKhqEnVCo9IY32lO9nnq_NUKrM5zYyjdiEHI_j7W31ZgF-dmdhKK2ys4mNoc5mhtbIlzmPiWotFPL57mePVKNod7WT0HCw2LB7X3HKVj1bf4X_9yHnq6-3Xq55raqAZ4YIV0_HymCYgp7WCs3jPM8izjXPZZT5uQ50GKPbyIgmxRob8SxSOIOReZBbUpIc5iG2ewpOC5wJkVTEiI_6NR3iEkcP2XHc-nw5LXGuKkhc6LDXOxbKHs_IPBwpN65u9eL_9pEuwYU2qGYv3Ci4DAu2uAJnR23awFX49g4d9NQe7H-v5iktPLGdGeKrs_ssJZ0MVriM-OopM5T9Y5yuBtvRRGHx8QmrnZ5Ec4fpea7dMVami4xh1F4f7P8oaS27ZqWdThwJ-t41eH8ir38dFotZYW8CiwXP0OjJ0FpfGK3jwDc6xYvWSonADuBxh4zEtLTspA4yTRyhNE8IQwm2P4D7fc1dR0XyhzpLHTCS1hhVyW9UYBN9MZoR2hvShZ3NqySQ8VCKKAzVAG44LPYP4RJdEYb2A5BHUNpXIIryoyXF5FNDVS4oABXY5qMGz3_td7LydiXE661_9_8enFvbGm0mm-vjjdtwHoPXyGVRLcFiXc7tHThjvtSTqrzbDEwGH04a5L8AEJl84Q
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NbtQwEB6Vgiou_P8sFDASPxeiTRwnTpAQopQVVdlVBVTqLTiOAyttsyXJgnqij4DE2_A4fRJm4iS0KnDrgVMOdhwn-TwztsffB_BApMqLpe87Ik6lI6RWTuy6meMqHcg4c7kJdSM2ISeTaGcn3lqCn91ZGEqr7GxiY6izuaY18iHnEVGt-UIO8zYtYmt99Hzvs0MKUrTT2slpWIhsmv2vOH2rnm2s479-yPno1fuXr51WYcDRAULXUVGsMWRBr2uE4lGeZyHniucyzNxcecqP0IVkRJlitAl5FsY4m5G5lxtSlQxyH9s9A2elCAJSTxjzcb--Q7zi6C07vluXD9MS562ChIaOesATYe3J7MyjUXPj9kYX_-cPdgkutME2e2FHx2VYMsUVWBm36QRX4ds7dNwzc3jwvVqktCDFdueIu84fsJT0M1hhM-Wrp0xTVpC2ehtsVxG1xccnrLY6E80duue_tsdbmSoyhtF8fXjwo6Q17pqVZja15Oj712D7VF7_OiwX88LcBBYJnqExlL4xrtBKRZ6rVYoXpeJYeGYAjzuUJLqlayfVkFliiaZ5QnhKsP0B3O9r7lmKkj_UWe1AkrRGqkp-IwSb6IvRvNCekSrMfFElnowCKULfjwdww-KyfwiX6KIw5B-APIbYvgJRlx8vKaafGgpzQYGpwDYfNdj-a7-TtbdrPl5v_bv_92AFsZ282Zhs3obzGNOGNrlqFZbrcmHuwDn9pZ5W5d1mjDL4cNoY_wW4HoWn
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=Single%E2%80%90subject+morphological+brain+networks%3A+connectivity+mapping%2C+topological+characterization+and+test%E2%80%93retest+reliability&rft.jtitle=Brain+and+behavior&rft.au=Wang%2C+Hao&rft.au=Jin%2C+Xiaoqing&rft.au=Zhang%2C+Ye&rft.au=Wang%2C+Jinhui&rft.date=2016-04-01&rft.pub=John+Wiley+and+Sons+Inc&rft.eissn=2162-3279&rft.volume=6&rft.issue=4&rft_id=info:doi/10.1002%2Fbrb3.448&rft_id=info%3Apmid%2F27088054&rft.externalDocID=PMC4782249
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2162-3279&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2162-3279&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2162-3279&client=summon