The DOI matches its Crossref record; 107 of the 107 references it cites were found in a registry or an index.
Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease
Open accessAbstract
Recent evidence suggests that some brain areas act as hubs interconnecting distinct, functionally specialized systems. These nexuses are intriguing because of their potential role in integration and also because they may augment metabolic cascades relevant to brain disease. To identify regions of high connectivity in the human cerebral cortex, we applied a computationally efficient approach to map the degree of intrinsic functional connectivity across the brain. Analysis of two separate functional magnetic resonance imaging datasets (each n = 24) demonstrated hubs throughout heteromodal areas of association cortex. Prominent hubs were located within posterior cingulate, lateral temporal, lateral parietal, and medial/lateral prefrontal cortices. Network analysis revealed that many, but not all, hubs were located within regions previously implicated as components of the default network. A third dataset ( n = 12) demonstrated that the locations of hubs were present across passive and active task states, suggesting that they reflect a stable property of cortical network architecture. To obtain an accurate reference map, data were combined across 127 participants to yield a consensus estimate of cortical hubs. Using this consensus estimate, we explored whether the topography of hubs could explain the pattern of vulnerability in Alzheimer's disease (AD) because some models suggest that regions of high activity and metabolism accelerate pathology. Positron emission tomography amyloid imaging in AD ( n = 10) compared with older controls ( n = 29) showed high amyloid-β deposition in the locations of cortical hubs consistent with the possibility that hubs, while acting as critical way stations for information processing, may also augment the underlying pathological cascade in AD.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Additionally, studies suggest that AD pathogenesis targets high-traffic hub regions in the brain, spreading from epicenters to secondary networks as the disease progresses (Buckner et al., 2009; Dai et al., 2015; Mallio et al., 2015; Stam et al., 2009; Zhou, Gennatas, Kramer, Miller, & Seeley, 2012; Zhou & Seeley, 2014).”
From Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease (2018) · cites this paper as Buckner et al., 2009
Experimental — accuracy not yet measuredIn the citing paper
“These findings add to the growing notion that disturbances in large-scale networks take center stage in determining cognitive decline. 182,183”
From Vascular Cognitive Impairment (2017) · cites this paper as [182]
In the citing paper
“In addition, the precuneus/posterior cingulate cortex (PCC), the main functional connectivity hub in the resting brain (Buckner et al., 2009), was selected as seed region (center voxel =À2, À50, 36, see Supporting Information).”
From Oxytocin effects on complex brain networks are moderated by experiences of maternal love withdrawal (2013) · cites this paper as Buckner et al., 2009
In the citing paper
“The PCC is considered a functional connectivity hub because of its high degree of connectivity with other brain regions (Buckner et al., 2009).”
From Oxytocin effects on complex brain networks are moderated by experiences of maternal love withdrawal (2013) · cites this paper as Buckner et al., 2009
In the citing paper
“Emerging evidence indicates that AD vulnerability is strongly associated with hyperconnectivity, augmented synaptic and metabolic activities, as well as functional plasticity 52 .”
From A Chronological Atlas of Natural Selection in the Human Genome during the Past Half-million Years (2015) · cites this paper as [52]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
Brainard, D. H. (1997) · Spatial Vision · doi:10.1163/156856897x00357 · Cited by 20,012
Social Network Analysis: Methods and Applications
Wasserman, S. S., & Faust, K. (1994) · Cited by 18,005
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557
Centrality in social networks conceptual clarification
Freeman, L. C. (1978) · Social Networks · doi:10.1016/0378-8733(78)90021-7 · Cited by 17,062
Community structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
A set of measures of centrality based upon betweenness
Freeman, L. (1977) · Sociometry · doi:10.2307/3033543 · Cited by 10,557
The Clinical Dementia Rating (CDR): Current version and scoring rules
Morris, J. C. (1993) · Neurology · doi:10.1212/wnl.43.11.2412-a · Cited by 9,882
Research works printed but not linked: 2
- 10.2466/pms.1966.23.2.399 · DOI · Paper page
- Ungerleider LG Mishkin M (1982) in Analysis of visual behavior, Two cortical visual systems, eds Ingle DJ Goodale MA Mansfield RJW (MIT, Cambridge, MA), pp 549–586.
Cited by
Sorted by citations
Items 401–500 of 1,024
Resting-state networks in the macaque at 7T
Hutchison, R. M., Leung, L.-W. S., Mirsattari, S. M., Gati, J. S., Menon, R. S., & Everling, S. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.063 · Cited by 141
Huang, Z., Obara, N., Davis, H., Pokorny, J., & Northoff, G. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.01.025 · Cited by 146
Wang, Y., Kang, J., Kemmer, P. B., & Guo, Y. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00123 · Cited by 141
Pereira, J. B., Mijalkov, M., Kakaei, E., Mecocci, P., Vellas, B., Tsolaki, M., Kłoszewska, I., Soininen, H., Spenger, C., Lovestone, S., et al. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw128 · Cited by 142
Functional network integrity presages cognitive decline in preclinical Alzheimer disease
Buckley, R. F., Schultz, A. P., Hedden, T., Papp, K. V., Hanseeuw, B. J., Marshall, G. A., Sepulcre, J., Smith, E. E., Rentz, D. M., Johnson, K. A., et al. (2017) · Neurology · doi:10.1212/wnl.0000000000004059 · Cited by 145
Self-citationThe multifaceted abstract brain
Desai, R. H., Reilly, M., & van Dam, W. O. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2017.0122 · Cited by 153
Molecular properties underlying regional vulnerability to Alzheimer’s disease pathology
Grothe, M. J., Sepulcre, J., Gonzalez-Escamilla, G., Jelistratova, I., Schöll, M., Hansson, O., Teipel, S. J., & Initiative, A. D. N. (2018) · Brain · doi:10.1093/brain/awy189 · Cited by 142
Self-citationMueller, S., Keeser, D., Samson, A. C., Kirsch, V., Blautzik, J., Grothe, M. J., Erat, O., Hegenloh, M., Coates, U., Reiser, M. F., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067329 · Cited by 148
Zhang, H., Chen, X., Shi, F., Li, G., Kim, M., Giannakopoulos, P., Haller, S., & Shen, D. G. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160092 · Cited by 142
Focal temporal pole atrophy and network degeneration in semantic variant primary progressive aphasia
Collins, J. A., Montal, V., Hochberg, D., Quimby, M., Mandelli, M. L., Makris, N., Seeley, W. W., Gorno‐Tempini, M. L., & Dickerson, B. C. (2016) · Brain · doi:10.1093/brain/aww313 · Cited by 145
Self-projection and the default network in frontotemporal dementia
Irish, M., Piguet, O., & Hodges, J. R. (2012) · Nature Reviews Neurology · doi:10.1038/nrneurol.2012.11 · Cited by 140
Stevens, M. C. G. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.07.027 · Cited by 145
Tai, X. Y., Bernhardt, B. C., Thom, M., Thompson, P. J., Baxendale, S., Koepp, M. J., & Bernasconi, N. (2017) · Neuropathology and Applied Neurobiology · doi:10.1111/nan.12458 · Cited by 145
Abnormal dynamic functional connectivity in Alzheimer’s disease
Gu, Y., Lin, Y., Huang, L., Ma, J., Zhang, J., Xiao, Y., Dai, Z., & Initiative, A. D. N. (2020) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.13387 · Cited by 143
Development of the Brain’s Functional Network Architecture
Vogel, A. C., Power, J. D., Petersen, S. E., & Schlaggar, B. L. (2010) · Neuropsychology Review · doi:10.1007/s11065-010-9145-7 · Cited by 143
Altered Network Topologies and Hub Organization in Adults with Autism: A Resting-State fMRI Study
Itahashi, T., Yamada, T., Watanabe, H., Nakamura, M., Jimbo, D., Shioda, S., Toriizuka, K., Kato, N., & Hashimoto, R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094115 · Cited by 143
Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients
Li, X., Zhu, D., Jiang, X., Jin, C., Zhang, X., Guo, L., Zhang, J., Hu, X. P., Li, L., & Liu, T. (2013) · Human Brain Mapping · doi:10.1002/hbm.22290 · Cited by 139
Interactions between the Salience and Default-Mode Networks Are Disrupted in Cocaine Addiction
Liang, X., Yong He, A., Salmeron, B. J., Gu, H., Stein, E. A., & Yang, Y. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.3188-14.2015 · Cited by 138
Markett, S. A., Reuter, M., Montag, C., Voigt, G., Lachmann, B., Rudorf, S., Elger, C. E., & Weber, B. (2013) · Human Brain Mapping · doi:10.1002/hbm.22285 · Cited by 147
Biomarkers for Alzheimer's disease therapeutic trials
Hampel, H. J., Wilcock, G. K., Andrieu, S., Aisen, P. S. S., Blennow, K., Broich, K., Carrillo, M. C., Fox, N. C., Frisoni, G. B., Isaac, M., et al. (2010) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2010.11.005 · Cited by 140
Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders
Hettwer, M. D., Larivière, S., Park, B., van den Heuvel, O. A., Schmaal, L., Andreassen, O. A., Ching, C. R. K., Hoogman, M., Buitelaar, J. K., van Rooij, D., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34367-6 · Cited by 138
Attention‐related networks in Alzheimer's disease: A resting functional MRI study
Li, R., Wu, X., Fleisher, A., Reiman, E. M., Chen, K., & Li, Y. (2011) · Human Brain Mapping · doi:10.1002/hbm.21269 · Cited by 146
Sampath, D., Sathyanesan, M., & Newton, S. S. (2017) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s136122 · Cited by 145
Uehara, T., Yamasaki, T., Okamoto, T., Koike, T., Kan, S., Miyauchi, S., Kira, J.-I., & Tobimatsu, S. (2014) · Cerebral Cortex · doi:10.1093/cercor/bht004 · Cited by 136
Chen, B., Xu, T., Zhou, C., Wang, L., Yang, N., Wang, Z., Dong, H.-M., Yang, Z., Zang, Y., Zuo, X., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0144963 · Cited by 136
Intrinsic functional network architecture of human semantic processing: Modules and hubs
Xu, Y., Lin, Q., Han, Z., Yong He, A., & Bi, Y. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.004 · Cited by 139
Reduced Hippocampal Functional Connectivity During Episodic Memory Retrieval in Autism
Cooper, R. A., Richter, F. R., Bays, P. M., Plaisted-Grant, K. C., Baron‐Cohen, S., & Simons, J. S. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw417 · Cited by 134
Changes in whole-brain functional networks and memory performance in aging
Sala‐Llonch, R., Junqué, C., Arenaza‐Urquijo, E. M., Dídac Vidal-piñeiro, A., Valls‐Pedret, C., Palacios, E. M., Doménech, S., Salvà, A., Bargalló, N., & Bartrés‐Faz, D. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.04.007 · Cited by 141
Hampel, H. J., Lista, S., & Khachaturian, Z. S. (2012) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2012.05.2116 · Cited by 142
Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia
Dong, D., Yao, D., Wang, Y., Hong, S., Genon, S., Xin, F., Jung, K., He, H., Chang, X., Duan, M., et al. (2021) · Psychological Medicine · doi:10.1017/s0033291721002129 · Cited by 138
Self-citationImpaired and facilitated functional networks in temporal lobe epilepsy
Maccotta, L., He, B. J., Snyder, A. Z., Eisenman, L. N., Benzinger, T. L. S., Ances, B. M., Corbetta, M., & Hogan, R. E. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.06.011 · Cited by 138
Local molecular and global connectomic contributions to cross-disorder cortical abnormalities
Hansen, J. Y., Shafiei, G., Vogel, J. W., Smart, K. A., Bearden, C. E., Hoogman, M., Franke, B., van Rooij, D., Buitelaar, J. K., McDonald, C. R., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32420-y · Cited by 130
López‐Sanz, D., Bruña, R., Garcés, P., Martín-Buro, M. C., Walter, S., Delgado‐Losada, M. L., Montenegro, M., López-Higes, R., Marcos, A., & Maestú, F. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00109 · Cited by 129
Forebrain-dominant deficit in cerebrovascular reactivity in Alzheimer's disease
Yezhuvath, U. S., Uh, J., Cheng, Y., Martin‐Cook, K., Weiner, M., Diaz-Arrastia, R. R., van Osch, M. J. P., & Lu, H. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.02.005 · Cited by 131
Within-patient correspondence of amyloid-β and intrinsic network connectivity in Alzheimer’s disease
Myers, N., Pasquini, L., Göttler, J., Grimmer, T., Koch, K., Ortner, M., Neitzel, J., Mühlau, M., Förster, S., Kurz, A., et al. (2014) · Brain · doi:10.1093/brain/awu103 · Cited by 132
“…et al., 2009; Bero et al., 2012; Zhou et al., 2012); and that (ii) amyloid-b pathology is accelerated by local stress caused by a lifetime of increased metabolism and intrinsic activity and connectivity (Greicius et al., 2004; Buckner et al., 2005, 2009; Bero et al., 2011; Drzezga et al., 2011).”
InfluentialSingle-Subject Grey Matter Graphs in Alzheimer's Disease
Tijms, B. M., Möller, C., Vrenken, H., Wink, A. M., de Haan, W., van der Flier, W. M., Stam, C. J., Scheltens, P., & Barkhof, F. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058921 · Cited by 132
Wesson, D. W., Borkowski, A. H., Landreth, G. E., Nixon, R. A., Levy, E., & Wilson, D. A. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.2085-11.2011 · Cited by 134
Brain imaging in the study of Alzheimer's disease
Reiman, E. M., & Jagust, W. J. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.075 · Cited by 139
Brown, J. A., Rudie, J. D., Bandrowski, A. E., Horn, J. D. V., & Bookheimer, S. Y. (2012) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2012.00028 · Cited by 133
Disrupted cortical hubs in functional brain networks in social anxiety disorder
Liu, F., Zhu, C., Wang, Y., Guo, W., Li, M., Wang, W., Long, Z., Meng, Y., Cui, Q., Zeng, L., et al. (2014) · Clinical Neurophysiology · doi:10.1016/j.clinph.2014.11.014 · Cited by 130
Iturria‐Medina, Y., Carbonell, F., Sotero, R. C., Chouinard-Decorte, F., & Evans, A. C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.058 · Cited by 130
Prefrontal connectomics: From anatomy to human imaging
Haber, S. N., Liu, H., Seidlitz, J., & Bullmore, E. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-021-01156-6 · Cited by 129
Self-citationFunctional connectivity changes differ in early and late-onset alzheimer's disease
Gour, N., Félician, O., Didic, M., Koric, L., Guériot, C., Chanoine, V., Sylviane Confort-gouny, A., Guye, M., Ceccaldi, M. P., & Ranjeva, J. (2013) · Human Brain Mapping · doi:10.1002/hbm.22379 · Cited by 129
Jung, M., Kosaka, H., Saito, D. N., Ishitobi, M., Morita, T., Inohara, K., Asano, M., Arai, S., Munesue, T., Tomoda, A., et al. (2014) · Molecular Autism · doi:10.1186/2040-2392-5-35 · Cited by 128
Genetic control of functional brain network efficiency in children
van den Heuvel, M. P., van Soelen, I. L. C., Stam, C. J., Kahn, R. S., Boomsma, D. I., & Hulshoff Pol, H. E. (2013) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2012.06.007 · Cited by 127
Binnewijzend, M. A. A., Adriaanse, S. M., van der Flier, W. M., Teunissen, C. E., de Munck, J. C., Stam, C. J., Scheltens, P., van Berckel, B. N. M., Barkhof, F., & Wink, A. M. (2013) · Human Brain Mapping · doi:10.1002/hbm.22335 · Cited by 129
Mapping the Alzheimer’s Brain with Connectomics
Xie, T., & Yong He, A. (2012) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2011.00077 · Cited by 129
Corticocortical Evoked Potentials Reveal Projectors and Integrators in Human Brain Networks
Keller, C. J., Honey, C. J., Entz, L., Bickel, S., Groppe, D. M., Toth, E., Ulbert, I., Lado, F. A., & Mehta, A. D. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4289-13.2014 · Cited by 130
Mapping Individual Brain Networks Using Statistical Similarity in Regional Morphology from MRI
Kong, X., Liu, Z., Huang, L., Wang, X., Yang, Z., Zhou, G., Zhen, Z., & Liu, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141840 · Cited by 121
Dídac Vidal-piñeiro, A., eValls-Pedret, C., Fernández‐Cabello, S., Arenaza‐Urquijo, E. M., Sala-Llonch, R., Solana, E., Bargalló, N., Junqué, C., Ros, E., & Bartrés‐Faz, D. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00256 · Cited by 140
Disrupted Resting-State Functional Connectivity in Nonmedicated Bipolar Disorder
Wang, Y., Zhong, S., Jia, Y., Sun, Y., Wang, B., Liu, T., Pan, J., & Huang, L. (2016) · Radiology · doi:10.1148/radiol.2016151641 · Cited by 129
Esposito, R., Cieri, F., Chiacchiaretta, P., Cera, N., Lauriola, M., Giannantonio, M. D., Tartaro, A., & Ferretti, A. (2017) · Brain Imaging and Behavior · doi:10.1007/s11682-017-9686-y · Cited by 133
Microstructural white matter changes in cognitively normal individuals at risk of amnestic MCI
Zhuang, L., Sachdev, P. S., Trollor, J. N., Kochan, N. A., Reppermund, S., Brodaty, H., & Wen, W. (2012) · Neurology · doi:10.1212/wnl.0b013e3182661f4d · Cited by 125
Wig, G. S., Laumann, T. O., Cohen, A. L., Power, J. D., Nelson, S. M., Glasser, M. F., Miezin, F. M., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht056 · Cited by 126
Tang, W., Jbabdi, S., Zhu, Z., Cottaar, M., Grisot, G., Lehman, J. F., Yendiki, A., & Haber, S. N. (2019) · eLife · doi:10.7554/elife.43761 · Cited by 127
Jiang, L., Xu, T., He, Y., Hou, X., Wang, J., Cao, X., Wei, G., Yang, Z., Yong He, A., & Zuo, X. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0795-8 · Cited by 125
Changes in functional and structural brain connectome along the Alzheimer’s disease continuum
Filippi, M., Basaia, S., Canu, E., Imperiale, F., Magnani, G., Falautano, M., Comı, G., Falini, A., & Agosta, F. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0067-8 · Cited by 120
From static to temporal network theory: Applications to functional brain connectivity
Thompson, W. H., Brantefors, P., & Fransson, P. (2017) · Network Neuroscience · doi:10.1162/netn_a_00011 · Cited by 128
Information processing architecture of functionally defined clusters in the macaque cortex
Shen, K., Bezgin, G., Hutchison, R. M., Gati, J. S., Menon, R. S., Everling, S., & McIntosh, A. R. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.2709-12.2012 · Cited by 123
Zhou, B., Liu, Y., Zhang, Z., An, N., Yao, H., Wang, P., Wang, L., Zhang, X., & Jiang, T. (2013) · Current Alzheimer Research · doi:10.2174/15672050113109990146 · Cited by 124
Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders
de Lange, S. C., Scholtens, L. H., Initiative, A. D. N., van den Berg, L. H., Boks, M. P., Bozzali, M., Cahn, W., Dannlowski, U., Durston, S., Geuze, E., et al. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0659-6 · Cited by 122
Lehmann, M., Madison, C. M., Ghosh, P. M., Seeley, W. W., Mormino, E., Greicius, M. D., Gorno-Tempini, M. L., Kramer, J. H., Miller, B. L., Jagust, W. J., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1221536110 · Cited by 122
Pujol, J., Macià, D., Garcia‐Fontanals, A., Blanco‐Hinojo, L., López‐Solà, M., Garcia-Blanco, S., Poca‐Dias, V., Harrison, B. J., Contreras‐Rodríguez, O., Monfort, J., et al. (2014) · Pain · doi:10.1016/j.pain.2014.04.028 · Cited by 129
Tu, Y., Jung, M., Gollub, R. L., Napadow, V., Gerber, J. A., Ortiz, A., Lang, C., Mawla, I., Shen, W., Chan, S., et al. (2019) · Pain · doi:10.1097/j.pain.0000000000001507 · Cited by 127
Modulating Gamma Oscillations Promotes Brain Connectivity to Improve Cognitive Impairment
Liu, C., Han, T., Xu, Z., Liu, J., Zhang, M., Du, J., Zhou, Q., Duan, Y., Li, Y., Wang, J., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab371 · Cited by 129
Thompson, W. H., & Fransson, P. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.022 · Cited by 123
Distinct clinical and metabolic deficits in PCA and AD are not related to amyloid distribution
Rosenbloom, M. H., Alkalay, A., Agarwal, N., Baker, S. L., O’Neil, J. P., Janabi, M., Yen, I. V., Growdon, M. E., Jang, J., Madison, C. M., et al. (2011) · Neurology · doi:10.1212/wnl.0b013e31821cccad · Cited by 125
Connectivity pattern changes in default-mode network with deep non-REM and REM sleep
Koike, T., Kan, S., Misaki, M., & Miyauchi, S. (2011) · Neuroscience Research · doi:10.1016/j.neures.2010.12.018 · Cited by 126
Boraxbekk, C., Salami, A., Wåhlin, A., & Nyberg, L. H. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.010 · Cited by 127
Functional Connectivity Bias in the Prefrontal Cortex of Psychopaths
Contreras‐Rodríguez, O., Pujol, J., Batalla, I., Harrison, B. J., Soriano‐Mas, C., Deus, J., López‐Solà, M., Macià, D., Pera, V., Hernández‐Ribas, R., et al. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.03.007 · Cited by 121
Brain Signal Variability is Parametrically Modifiable
Garrett, D. D., McIntosh, A. R., & Grady, C. L. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht150 · Cited by 122
Taylor, P., Hobbs, J. N., Burroni, J., & Siegelmann, H. T. (2015) · Scientific Reports · doi:10.1038/srep18112 · Cited by 125
Angular default mode network connectivity across working memory load
Vatansever, D., Manktelow, A. E., Sahakian, B. J., Menon, D. K., & Stamatakis, E. A. (2016) · Human Brain Mapping · doi:10.1002/hbm.23341 · Cited by 120
Dimitriadis, S. I., Salis, C. I., Tarnanas, I., & Linden, D. E. J. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00028 · Cited by 117
Task-positive Functional Connectivity of the Default Mode Network Transcends Task Domain
Elton, A., & Gao, W. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00859 · Cited by 119
Elman, J. A., Madison, C. M., Baker, S. L., Vogel, J. W., Marks, S. M., Crowley, S., O’Neil, J. P., & Jagust, W. J. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu259 · Cited by 122
Hua, X., Hibar, D. P., Ching, C. R. K., Boyle, C. P., Rajagopalan, P., Gutman, B. A., Leow, A., Toga, A. W., Jack, C. R., Harvey, D. J., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.086 · Cited by 114
de Waal, H., Stam, C. J., Lansbergen, M. M., Wieggers, R. L., Kamphuis, P. J. G. H., Scheltens, P., Maestú, F., & van Straaten, E. C. W. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086558 · Cited by 120
Neurocognitive biases and the patterns of spontaneous correlations in the human cortex
Harmelech, T., & Malach, R. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.014 · Cited by 122
Zhang, P., Li, L. S., Lin, L., Hu, P. C., Shi, J., He, Y., Zhu, L., Zhou, Y., & Wang, L. V. (2017) · Journal of Biophotonics · doi:10.1002/jbio.201700024 · Cited by 122
Animal Functional Magnetic Resonance Imaging: Trends and Path Toward Standardization
Mandino, F., Cerri, D. H., Garin, C. M., Straathof, M., van Tilborg, G. A. F., Chakravarty, M. M., Dhénain, M., Dijkhuizen, R. M., Gozzi, A., Heß, A., et al. (2020) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00078 · Cited by 115
Ruppert, M. C., Greuel, A., Tahmasian, M., Schwartz, F., Stürmer, S., Maier, F., Hammes, J., Tittgemeyer, M., Timmermann, L., van Eimeren, T., et al. (2020) · Brain · doi:10.1093/brain/awaa019 · Cited by 119
Connectomic Intermediate Phenotypes for Psychiatric Disorders
Fornito, A., & Bullmore, E. T. (2012) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2012.00032 · Cited by 121
Interhemispheric temporal lobe connectivity predicts language impairment in adolescents born preterm
Northam, G. B., Liegeois, F. J., Tournier, J., Croft, L. J., Johns, P., Chong, W. K., Wyatt, J. S., & Baldeweg, T. (2012) · Brain · doi:10.1093/brain/aws276 · Cited by 120
Age-related functional reorganization, structural changes, and preserved cognition
David Meunier, A., Stamatakis, E. A., & TYLER, L. K. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.07.003 · Cited by 121
Pereira, J. B., Westman, E., & Hansson, O. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.06.002 · Cited by 120
Category-Selective Background Connectivity in Ventral Visual Cortex
Norman-Haignere, S., McCarthy, G., Chun, M. M., & Turk‐Browne, N. B. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr118 · Cited by 122
Dynamic changes of ICA‐derived EEG functional connectivity in the resting state
Chen, J., Ros, T., & Gruzelier, J. H. (2012) · Human Brain Mapping · doi:10.1002/hbm.21475 · Cited by 121
In vivo characterization of the early states of the amyloid-beta network
Sepulcre, J., Sabuncu, M. R., Becker, A., Sperling, R., & Johnson, K. A. (2013) · Brain · doi:10.1093/brain/awt146 · Cited by 117
Self-citationHippocampal activation is associated with longitudinal amyloid accumulation and cognitive decline
Leal, S. L., Landau, S. M., Bell, R. K., & Jagust, W. J. (2017) · eLife · doi:10.7554/elife.22978 · Cited by 117
Meta-analysis of functional network alterations in Alzheimer's disease: Toward a network biomarker
Jacobs, H. I. L., Raduà, J., Lückmann, H. C., & Sack, A. T. (2013) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.03.009 · Cited by 116
From Intrinsic Firing Properties to Selective Neuronal Vulnerability in Neurodegenerative Diseases
Roselli, F., & Caroni, P. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.063 · Cited by 117
Mormino, E. C., & Papp, K. V. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-179928 · Cited by 124
Impaired cortico-striatal functional connectivity in prodromal Huntington's Disease
Unschuld, P. G., Joel, S. E., Liu, X., Shanahan, M. R., Margolis, R. L., Biglan, K., Bassett, S. S., Schretlen, D. J., Redgrave, G. W., van Zijl, P. C. M., et al. (2012) · Neuroscience Letters · doi:10.1016/j.neulet.2012.02.095 · Cited by 117
Sun, Y., Dai, Z., Li, Y., Sheng, C., Li, H., Wang, X., Chen, X., He, Y., & Han, Y. (2016) · Radiology · doi:10.1148/radiol.2016151771 · Cited by 116
Aberrant Network Activity in Schizophrenia
Hunt, M. J., Kopell, N., Traub, R. D., & Whittington, M. A. (2017) · Trends in Neurosciences · doi:10.1016/j.tins.2017.04.003 · Cited by 118
Short-range connections in the developmental connectome during typical and atypical brain maturation
Ouyang, M., Kang, H., Detre, J. A., Roberts, T. P. L., & Huang, H. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.10.007 · Cited by 120
André, C., Rehel, S., Kuhn, E., Landeau, B., Moulinet, I., Touron, E., Ourry, V., Du, G. L., Mézenge, F., Tomadesso, C., et al. (2020) · JAMA Neurology · doi:10.1001/jamaneurol.2020.0311 · Cited by 126
Sleep and the functional connectome
Picchioni, D., Duyn, J. H., & Horovitz, S. G. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.067 · Cited by 115
van Dellen, E., van der Kooi, A. W., Numan, T., Koek, H. L., Klijn, F. A., Buijsrogge, M. P., Stam, C. J., & Slooter, A. J. C. (2014) · Anesthesiology · doi:10.1097/aln.0000000000000329 · Cited by 119
Metadata
- Authors
- Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., Andrews-Hanna, J. R., Sperling, R. A., & Johnson, K. A.
- Affiliations
- Harvard University (US)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
- Brigham and Women's Hospital (US)
- ORCID
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2009-02-11
- Volume
- 29
- Issue
- 6
- Pages
- 1860-1873
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC2750039
- OpenAlex
- W2126650951
- Cited by
- 2,862
- References
- 91
- Citations by year
- 2026: 82 · 2025: 128 · 2024: 139 · 2023: 143 · 2022: 162 · 2021: 212 · 2020: 167 · 2019: 199 · 2018: 208 · 2017: 223 · 2016: 192 · 2015: 211 · 2014: 219 · 2013: 182 · 2012: 148
- FWCI
- 63.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Howard Hughes Medical Institute
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- National Institute on Aging (AG021910, R01AG021910, AG-027435-S1, R01AG027435, R01AG034556)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- cortical hubs, intrinsic functional connectivity, default mode network, association cortex, posterior cingulate cortex, prefrontal cortex, parietal cortex, temporal cortex, network stability, Alzheimer's disease, amyloid-beta deposition, brain network architecture
- MeSH terms
- Adolescent, Aged, Aged, 80 and over, Alzheimer Disease, Brain Mapping, Cerebral Cortex, Female, Humans, Male, Middle Aged, Nerve Net, Neural Pathways, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
