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Altered functional connectivity in early Alzheimer's disease: A resting‐state fMRI study
Open accessAbstract
Previous studies have led to the proposal that patients with Alzheimer's disease (AD) may have disturbed functional connectivity between different brain regions. Furthermore, recent resting‐state functional magnetic resonance imaging (fMRI) studies have also shown that low‐frequency (<0.08 Hz) fluctuations (LFF) of the blood oxygenation level‐dependent signals were abnormal in several brain areas of AD patients. However, few studies have investigated disturbed LFF connectivity in AD patients. By using resting‐state fMRI, this study sought to investigate the abnormal functional connectivities throughout the entire brain of early AD patients, and analyze the global distribution of these abnormalities. For this purpose, the authors divided the whole brain into 116 regions and identified abnormal connectivities by comparing the correlation coefficients of each pair. Compared with healthy controls, AD patients had decreased positive correlations between the prefrontal and parietal lobes, but increased positive correlations within the prefrontal lobe, parietal lobe, and occipital lobe. The AD patients also had decreased negative correlations (closer to zero) between two intrinsically anti‐correlated networks that had previously been found in the resting brain. By using resting‐state fMRI, our results supported previous studies that have reported an anterior–posterior disconnection phenomenon and increased within‐lobe functional connectivity in AD patients. In addition, the results also suggest that AD may disturb the correlation/anti‐correlation effect in the two intrinsically anti‐correlated networks. Hum Brain Mapp 2006. © 2006 Wiley‐Liss, Inc.
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Citations
Cites
Control of goal-directed and stimulus-driven attention in the brain
Corbetta, M., & Shulman, G. L. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn755 · Cited by 12,881
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
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
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308627101 · Cited by 3,684
Verbal fluency performance in dementia of the Alzheimer’s type: a meta-analysis
Henry, J. D., Crawford, J. R., & Phillips, L. H. (2004) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2004.02.001 · Cited by 847 · cited 1×
“In addition, some studies have also shown that language and visuoconstructive abilities, whose integrity depended on frontal and parietal lobe interactions, were significantly impaired in AD patients [Backman et al., 2005; Brouwers et al., 1984; Henry et al., 2004].”
BackgroundCognitive impairment in preclinical Alzheimer's disease: a meta-analysis
Bäckman, L., Jones, S., Berger, A.-K., Laukka, E. J., & Small, B. J. (2005) · Neuropsychology · doi:10.1037/0894-4105.19.4.520 · Cited by 735
Detection of functional connectivity using temporal correlations in MR images
Hampson, M., Peterson, B. S., Skudlarski, P., Gatenby, J. C., & Gore, J. C. (2002) · Human Brain Mapping · doi:10.1002/hbm.10022 · Cited by 699
Cited by
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Conn : A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks
Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0073 · Cited by 4,885
Van Dijk, K. R. A., Hedden, T., Venkataraman, A., Evans, K. C., Lazar, S. W., & Buckner, R. L. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00783.2009 · Cited by 1,844
Default Mode Network Activity and Connectivity in Psychopathology
Whitfield-Gabrieli, S., & Ford, J. M. (2012) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032511-143049 · Cited by 1,465
Resting-state functional connectivity in neuropsychiatric disorders
Greicius, M. (2008) · Current Opinion in Neurology · doi:10.1097/wco.0b013e328306f2c5 · Cited by 1,180
Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease
Supekar, K., Menon, V., Rubin, D., Musen, M., & Greicius, M. D. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000100 · Cited by 1,148
Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease
Sorg, C., Riedl, V., Mühlau, M., Calhoun, V. D., Eichele, T., Läer, L., Drzezga, A., Förstl, H., Kurz, A., Zimmer, C., et al. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0708803104 · Cited by 1,036
Can structure predict function in the human brain?
Honey, C. J., Thivierge, J.-P., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.071 · Cited by 629
Rathore, S., Habes, M., Iftikhar, M. A., Shacklett, A., & Davatzikos, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.057 · Cited by 622
Sanz-Arigita, E. J., Schoonheim, M. M., Damoiseaux, J. S., Rombouts, S. A. R. B., Maris, E., Barkhof, F., Scheltens, P., & Stam, C. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013788 · Cited by 608
Brier, M. R., Thomas, J. B., Snyder, A. Z., Benzinger, T. L., Zhang, D., Raichle, M. E., Holtzman, D. M., Morris, J. C., & Ances, B. M. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.5698-11.2012 · Cited by 595
Functional brain connectivity using fMRI in aging and Alzheimer’s disease
Dennis, E. L., & Thompson, P. M. (2014) · Neuropsychology Review · doi:10.1007/s11065-014-9249-6 · Cited by 583
Network homogeneity reveals decreased integrity of default-mode network in ADHD
Uddin, L. Q., Kelly, A. M. C., Biswal, B. B., Margulies, D. S., Shehzad, Z., Shaw, D., Ghaffari, M., Rotrosen, J., Adler, L. A., Castellanos, F. X., et al. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.11.031 · Cited by 452
Age-related changes in the default mode network are more advanced in Alzheimer disease
Jones, D. T., Machulda, M. M., Vemuri, P., McDade, E. M., Zeng, G., Senjem, M. L., Gunter, J. L., Przybelski, S. A., Avula, R. T., Knopman, D. S., et al. (2011) · Neurology · doi:10.1212/wnl.0b013e318233b33d · Cited by 370
Lehmann, M., Ghosh, P. M., Madison, C., Laforce, R., Corbetta-Rastelli, C., Weiner, M. W., Greicius, M. D., Seeley, W. W., Gorno-Tempini, M. L., Rosen, H. J., et al. (2013) · Brain · doi:10.1093/brain/aws327 · Cited by 325
Schultz, A. P., Chhatwal, J. P., Hedden, T., Mormino, E. C., Hanseeuw, B. J., Sepulcre, J., Huijbers, W., LaPoint, M., Buckley, R. F., Johnson, K. A., et al. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3263-16.2017 · Cited by 330
Damoiseaux, J. S., Seeley, W. W., Zhou, J., Shirer, W. R., Coppola, G., Karydas, A., Rosen, H. J., Miller, B. L., Kramer, J. H., Greicius, M. D., et al. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.0305-12.2012 · Cited by 267
Zhang, Z., Liu, Y., Jiang, T., Zhou, B., An, N., Dai, H., Wang, P., Niu, Y., Wang, L., & Zhang, X. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.049 · Cited by 255
Self-citationUngar, L., Altmann, A., & Greicius, M. D. (2014) · Brain Imaging and Behavior · doi:10.1007/s11682-013-9272-x · Cited by 246
Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory
Khazaee, A., Ebrahimzadeh, A., & Babajani-Feremi, A. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2015.02.060 · Cited by 234
Hohenfeld, C., Werner, C. J., & Reetz, K. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2018.03.013 · Cited by 232
Cross-hemispheric functional connectivity in the human fetal brain
Thomason, M. E., Dassanayake, M. T., Shen, S., Katkuri, Y., Alexis, M., Anderson, A. L., Yeo, L., Mody, S., Hernandez-Andrade, E., Hassan, S. S., et al. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3004978 · Cited by 225
Functional connectivity MR imaging reveals cortical functional connectivity in the developing brain
Lin, W., Zhu, Q., Gao, W., Chen, Y., Toh, C.-H., Styner, M., Gerig, G., Smith, J. K., Biswal, B., & Gilmore, J. H. (2008) · American Journal of Neuroradiology · doi:10.3174/ajnr.a1256 · Cited by 222
Fleisher, A. S., Sherzai, A., Taylor, C., Langbaum, J. B. S., Chen, K., & Buxton, R. B. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.06.021 · Cited by 217
Disrupted Small-World Brain Networks in Moderate Alzheimer's Disease: A Resting-State fMRI Study
Zhao, X., Liu, Y., Wang, X., Liu, B., Xi, Q., Guo, Q., Jiang, H., Jiang, T., & Wang, P. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033540 · Cited by 215
Self-citationAlzheimer disease: new concepts on its neurobiology and the clinical role imaging will play
Jack, C. R. (2012) · Radiology · doi:10.1148/radiol.12110433 · Cited by 211
Real-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks
Ruiz, S., Buyukturkoglu, K., Rana, M., Birbaumer, N., & Sitaram, R. (2014) · Biological Psychology · doi:10.1016/j.biopsycho.2013.04.010 · Cited by 187
Functional network connectivity in the behavioral variant of frontotemporal dementia
Filippi, M., Agosta, F., Scola, E., Canu, E., Magnani, G., Marcone, A., Valsasina, P., Caso, F., Copetti, M., Comi, G., et al. (2013) · Cortex · doi:10.1016/j.cortex.2012.09.017 · Cited by 177
Canuet, L., Tellado, I., Couceiro, V., Fraile, C., Fernandez-Novoa, L., Ishii, R., Takeda, M., & Cacabelos, R. (2012) · PLoS ONE · doi:10.1371/journal.pone.0046289 · Cited by 163
Intrinsic functional connectivity variance and state‐specific under‐connectivity in autism
Chen, H., Nomi, J. S., Uddin, L. Q., Duan, X., & Chen, H. (2017) · Human Brain Mapping · doi:10.1002/hbm.23764 · Cited by 137
Risky Decisions and Their Consequences: Neural Processing by Boys with Antisocial Substance Disorder
Crowley, T. J., Dalwani, M. S., Mikulich-Gilbertson, S. K., Du, Y. P., Lejuez, C. W., Raymond, K. M., & Banich, M. T. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012835 · Cited by 142
Weissman-Fogel, I., Moayedi, M., Tenenbaum, H. C., Goldberg, M. B., Freeman, B. V., & Davis, K. D. (2011) · Pain · doi:10.1016/j.pain.2010.10.046 · Cited by 140
Visual short-term memory binding in Alzheimer's disease and depression
Parra, M. A., Abrahams, S., Logie, R. H., & Della Sala, S. (2010) · Journal of Neurology · doi:10.1007/s00415-010-5484-9 · Cited by 115
Li, Y., Zhu, Y., Nguchu, B. A., Wang, Y., Wang, H., Qiu, B., & Wang, X. (2020) · Autism Research · doi:10.1002/aur.2212 · Cited by 95
Bai, F., Watson, D. R., Shi, Y., Wang, Y., Yue, C., YuhuanTeng, Wu, D., Yuan, Y., & Zhang, Z. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024271 · Cited by 85
Dillen, K. N. H., Jacobs, H. I. L., Kukolja, J., von Reutern, B., Richter, N., Onur, Ö. A., Dronse, J., Langen, K.-J., & Fink, G. R. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.04.010 · Cited by 82
Pihlajamäki, M., & Sperling, R. A. (2009) · Behavioural Neurology · doi:10.1155/2009/276384 · Cited by 102
Wang, Z., Xia, M., Dai, Z., Liang, X., Song, H., He, Y., & Li, K. (2015) · Brain Structure and Function · doi:10.1007/s00429-013-0681-9 · Cited by 73
Self-citationFunctional connectivity and the sleep-deprived brain
Chee, M. W. L., & Zhou, J. (2019) · Progress in brain research · doi:10.1016/bs.pbr.2019.02.009 · Cited by 68
Criteria for the neuropathological diagnosis of dementing disorders: routes out of the swamp?
Jellinger, K. A. (2009) · Acta Neuropathologica · doi:10.1007/s00401-008-0466-z · Cited by 60
Hyperthermia-induced disruption of functional connectivity in the human brain network
Sun, G., Qian, S., Jiang, Q., Liu, K., Li, B., Li, M., Zhao, L., Zhou, Z., von Deneen, K. M., & Liu, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061157 · Cited by 58
Hafkemeijer, A., Möller, C., Dopper, E. G. P., Jiskoot, L. C., van den Berg-Huysmans, A. A., van Swieten, J. C., van der Flier, W. M., Vrenken, H., Pijnenburg, Y. A. L., Barkhof, F., et al. (2016) · Journal of Alzheimer’s Disease · doi:10.3233/jad-150695 · Cited by 57
Mohammadi-Nejad, A.-R., Hossein-Zadeh, G.-A., & Soltanian-Zadeh, H. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2681966 · Cited by 56
Disturbed Small-World Networks and Neurocognitive Function in Frontal Lobe Low-Grade Glioma Patients
Huang, Q., Zhang, R., Hu, X., Ding, S., Qian, J., Lei, T., Cao, X., Tao, L., Qian, Z., & Liu, H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094095 · Cited by 55
McCarthy, P., Benuskova, L., & Franz, E. A. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00301 · Cited by 48
Cluster-based statistics for brain connectivity in correlation with behavioral measures.
Han, C. E., Yoo, S. W., Seo, S. W., Na, D. L., & Seong, J.-K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0072332 · Cited by 49
Vanneste, S., Joos, K., Ost, J., & De Ridder, D. (2018) · Neurobiology of Stress · doi:10.1016/j.ynstr.2016.11.003 · Cited by 45
Predicting Brain Functional Connectivity Using Mobile Sensing
Obuchi, M., Huckins, J. F., Wang, W., daSilva, A., Rogers, C., Murphy, E., Hedlund, E., Holtzheimer, P., Mirjafari, S., & Campbell, A. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3381001 · Cited by 29
Metadata
- Authors
- Wang, K., Liang, M., Wang, L., Tian, L., Zhang, X., Li, K., & Jiang, T.
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2006-11-28
- Volume
- 28
- Issue
- 10
- Pages
- 967-978
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- OpenAlex
- W2171630967
- Cited by
- 732
- References
- 9
- FWCI
- 5.68
- Type
- journal article
- Language
- en
- Open access
- green
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