The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Age-related changes in the default mode network are more advanced in Alzheimer disease
Open accessAbstract
OBJECTIVE: To investigate age-related default mode network (DMN) connectivity in a large cognitively normal elderly cohort and in patients with Alzheimer disease (AD) compared with age-, gender-, and education-matched controls. METHODS: We analyzed task-free-fMRI data with both independent component analysis and seed-based analysis to identify anterior and posterior DMNs. We investigated age-related changes in connectivity in a sample of 341 cognitively normal subjects. We then compared 28 patients with AD with 56 cognitively normal noncarriers of the APOE ε4 allele matched for age, education, and gender. RESULTS: The anterior DMN shows age-associated increases and decreases in fontal lobe connectivity, whereas the posterior DMN shows mainly age-associated declines in connectivity throughout. Relative to matched cognitively normal controls, subjects with AD display an accelerated pattern of the age-associated changes described above, except that the declines in frontal lobe connectivity did not reach statistical significance. These changes survive atrophy correction and are correlated with cognitive performance. CONCLUSIONS: The results of this study indicate that the DMN abnormalities observed in patients with AD represent an accelerated aging pattern of connectivity compared with matched controls.
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?
In the citing paper
“Furthermore, abnormalities in the default mode network, which is regulated in part by PV-INs [27], have been observed in AD patients [28][29][30][31][32].”
From Parvalbumin interneuron vulnerability and brain disorders (2021) · cites this paper as [28]
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
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
Dissociable intrinsic connectivity networks for salience processing and executive control
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L., & Greicius, M. D. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.5587-06.2007 · Cited by 7,426
Correspondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347
Self-citationGreicius, 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
DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI
Yan (2010) · Frontiers in System Neuroscience · doi:10.3389/fnsys.2010.00013 · Cited by 3,475
A method for making group inferences from functional MRI data using independent component analysis
Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001) · Human Brain Mapping · doi:10.1002/hbm.1048 · Cited by 2,994
Toward discovery science of human brain function
Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S. M., Beckmann, C. F., Adelstein, J. S., Buckner, R. L., Colcombe, S., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0911855107 · Cited by 2,970
Neurodegenerative Diseases Target Large-Scale Human Brain Networks
Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L., & Greicius, M. D. (2009) · Neuron · doi:10.1016/j.neuron.2009.03.024 · Cited by 2,350
The global signal and observed anticorrelated resting state brain networks
Fox, M. D., Zhang, D., Snyder, A. Z., & Raichle, M. E. (2009) · Journal of Neurophysiology · doi:10.1152/jn.90777.2008 · Cited by 1,877
Research works printed but not linked: 1
- Jones DT, Vemuri P, Machulda MM, et al.. Age effect on functional connectivity. Presented at the American Academy of Neurology 62nd Annual Meeting, April 10–17 2010, Toronto, Canada. Abstract.
Cited by
Sorted by citations
Items 101–200 of 370
Brain network alteration in patients with temporal lobe epilepsy with cognitive impairment
Yang, H., Zhang, C., Liu, C., Yu, T., Zhang, G., Chen, N., & Li, K. (2018) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2018.01.024 · Cited by 62
Age‐dependent effects of brain stimulation on network centrality
Antonenko, D., Nierhaus, T., Meinzer, M., Prehn, K., Thielscher, A., Ittermann, B., & Flöel, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.038 · Cited by 55
Dementia with Lewy bodies: association of Alzheimer pathology with functional connectivity networks
Schumacher, J., Gunter, J. L., Przybelski, S. A., Jones, D. T., Graff‐Radford, J., Savica, R., Schwarz, C. G., Senjem, M. L., Jack, C. R., Lowe, V. J., et al. (2021) · Brain · doi:10.1093/brain/awab218 · Cited by 57
Self-citationMoretti, D. V., Paternicò, D., Binetti, G. G., Zanetti, O. O., & Frisoni, G. B. (2013) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2013.00063 · Cited by 58
Imaging-wide association study: integrating imaging endophenotypes in GWAS
Xu, Z., Wu, C., & Pan, W. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.036 · Cited by 56
Hu, Q., Li, Y., Wu, Y., Lin, X., & Zhao, X. (2022) · Human Brain Mapping · doi:10.1002/hbm.25863 · Cited by 56
Jockwitz, C., Caspers, S., Lux, S., Jütten, K., Schleicher, A., Eickhoff, S. B., Amunts, K., & Zilles, K. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1202-4 · Cited by 53
Assessing Brain Networks by Resting-State Dynamic Functional Connectivity: An fNIRS-EEG Study
Zhang, Y., & Zhu, C. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01430 · Cited by 54
Functional engagement of white matter in resting-state brain networks
Li, M., Gao, Y., Gao, F., Anderson, A. W., Ding, Z., & Gore, J. C. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117096 · Cited by 47
Disruption of Resting Functional Connectivity in Alzheimer’s Patients and At-Risk Subjects
Krajčovičová, L., Mareček, R., Mikl, M., & Rektorová, I. (2014) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-014-0491-3 · Cited by 51
Walker, K. A., Gross, A. L., Moghekar, A., Soldan, A., Pettigrew, C. A., Hou, X., Lu, H., Alfini, A. J., Bilgel, M., Miller, M. I., et al. (2020) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2020.01.006 · Cited by 52
Pievani, M., Pini, L., Ferrari, C., Pizzini, F. B., Galazzo, I. B., Cobelli, C., Cotelli, M., Manenti, R., & Frisoni, G. B. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-161105 · Cited by 52
Jannusch, K., Jockwitz, C., Bidmon, H. J., Moebus, S., Amunts, K., & Caspers, S. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00596 · Cited by 50
A posterior-to-anterior shift of brain functional dynamics in aging
Zhang, H., Lee, A., & Qiu, A. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1425-z · Cited by 48
Wells, R. E., Kerr, C., Dossett, M. L., Danhauer, S. C., Sohl, S. J., Sachs, B. C., Feeley, J. W., Wolkin, J., Wall, R., Kaptchuk, T., et al. (2019) · Journal of Alzheimer's Disease · doi:10.3233/jad-190191 · Cited by 47
Enhancement of posterior brain functional networks in bilingual older adults
de Frutos‐Lucas, J., López‐Sanz, D., Cuesta, P., Bruña, R., García, S. D. L. F., Serrano, N., López, M. E., Delgado‐Losada, M. L., López-Higes, R., Marcos, A., et al. (2019) · Bilingualism Language and Cognition · doi:10.1017/s1366728919000178 · Cited by 48
Orr, M. E., Kotkowski, E., Ramirez, P., Bair‐Kelps, D., Liu, Q., Brenner, C., Schmidt, M. S., Fox, P. T., Larbi, A., Tan, C. T. Y., et al. (2023) · GeroScience · doi:10.1007/s11357-023-00999-9 · Cited by 61
Meusel, L.-A., Kansal, N., Tchistiakova, E., Yuen, W., MacIntosh, B. J., Greenwood, C. E., & Anderson, N. D. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00148 · Cited by 49
Gao, J., Cheung, R. T. F., Chan, Y., Chu, L., Mak, H. K., & Lee, T. M. C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0090307 · Cited by 47
Disrupted functional connectivity in primary progressive apraxia of speech
Botha, H., Utianski, R. L., Whitwell, J. L., Duffy, J. R., Clark, H. M., Strand, E. A., Machulda, M. M., Tosakulwong, N., Knopman, D. S., Petersen, R. C., et al. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.02.036 · Cited by 49
Self-citationKobeleva, X., Firbank, M., Peraza, L. R., Gallagher, P., Thomas, A. J., Burn, D. J., O'Brien, J. T., & Taylor, J. (2017) · Cortex · doi:10.1016/j.cortex.2017.02.016 · Cited by 42
Sex differences in default mode network connectivity in healthy aging adults
Ficek, B., Horien, C. L., Ju, S., Xu, W., Li, N., Lacadie, C., Shen, X., Scheinost, D., Constable, R. T., & Fredericks, C. A. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac491 · Cited by 46
Manenti, R., Sandrini, M., Gobbi, E., Cobelli, C., Brambilla, M., Binetti, G. G., & Cotelli, M. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00401 · Cited by 46
Bray, N. W., Pieruccini‐Faria, F., Bartha, R., Doherty, T. J., Nagamatsu, L. S., & Montero‐Odasso, M. (2021) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2021.111493 · Cited by 47
Optogenetics: implications for Alzheimer’s disease research and therapy
Mirzayi, P., Shobeiri, P., Kalantari, A., Perry, G. L., & Rezaei, N. (2022) · Molecular Brain · doi:10.1186/s13041-022-00905-y · Cited by 45
Ratio of Aβ42/P-tau181p in CSF is associated with aberrant default mode network in AD
Li, X., Li, T., Andreasen, N., Wiberg, M. K., Westman, E., & Wahlund, L. (2013) · Scientific Reports · doi:10.1038/srep01339 · Cited by 42
He, L., Wang, X., Zhuang, K., & Qiu, J. (2020) · Neuroscience · doi:10.1016/j.neuroscience.2020.04.030 · Cited by 38
Hampton, O. L., Buckley, R. F., Manning, L. K., Scott, M. R., Properzi, M. J., Peña-Gómez, C., Jacobs, H. I. L., Chhatwal, J. P., Johnson, K. A., Sperling, R. A., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102407 · Cited by 44
Multivariate Pattern Analysis of fMRI in Breast Cancer Survivors and Healthy Women
Hosseini, S. M. H., & Kesler, S. R. (2013) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617713001173 · Cited by 40
Peña-Gómez, C., Solé‐Padullés, C., Clemente, I. C., Junqué, C., Bargalló, N., Bosch, B., Molinuevo, J. L., Valls‐Solé, J., Pascual‐Leone, Á., & Bartrés‐Faz, D. (2012) · PLoS ONE · doi:10.1371/journal.pone.0051833 · Cited by 39
Kim, J., Zhang, Y., & Pan, W. (2016) · Genetics · doi:10.1534/genetics.115.186502 · Cited by 39
A robust biomarker of large‐scale network failure in Alzheimer's disease
Wiepert, D. A., Lowe, V. J., Knopman, D. S., Boeve, B. F., Graff‐Radford, J., Petersen, R. C., Jack, C. R., & Jones, D. T. (2017) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2017.01.004 · Cited by 38
Self-citationHaneef, Z., Lenartowicz, A., Yeh, H. J., Engel, J., & Stern, J. M. (2014) · Journal of Visualized Experiments · doi:10.3791/51442 · Cited by 39
The thermodynamic brain and the evolution of intellect: the role of mental energy
Déli, É., & Kisvárday, Z. F. (2020) · Cognitive Neurodynamics · doi:10.1007/s11571-020-09637-y · Cited by 36
Marco, M. D., Manca, R., Mitolo, M., & Venneri, A. (2017) · Neural Plasticity · doi:10.1155/2017/4050536 · Cited by 38
Du, M., Liao, W., Lui, S., Huang, X., Li, F. Y., Kuang, W., Li, J., Chen, H.-F., Kendrick, K. M., & Gong, Q. (2015) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsv040 · Cited by 38
Melie‐García, L., Slater, D., Ruef, A., Sanabria-Díaz, G., Preisig, M., Kherif, F., Draganski, B., & Lutti, A. (2017) · Human Brain Mapping · doi:10.1002/hbm.23929 · Cited by 36
Sie, J.-H., Chen, Y.-H., Shiau, Y.-H., & Chu, W.-C. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00369 · Cited by 35
Cognitive stimulation of the default-mode network modulates functional connectivity in healthy aging
Marco, M. D., Meneghello, F., Duzzi, D., Rigon, J., Pilosio, C., & Venneri, A. (2015) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2015.12.001 · Cited by 39
Blackburn, D. J., Zhao, Y., Marco, M. D., Bell, S., He, F., Wei, H., Lawrence, S., Unwin, Z., Blyth, M., Angel, J., et al. (2018) · Brain Sciences · doi:10.3390/brainsci8070134 · Cited by 36
Amyloid and FDG PET of Successful Cognitive Aging: Global and Cingulate-Specific Differences
Baran, T. M., Lin, F. V., & Initiative, A. D. N. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-180360 · Cited by 37
Paolis, M. L. D., Paoletti, I., Zaccone, C., Capone, F., D’Amelio, M., & Krashia, P. (2024) · Translational Neurodegeneration · doi:10.1186/s40035-024-00423-y · Cited by 44
Brown, C., Jiang, Y., Smith, C. D., & Gold, B. T. (2018) · Cortex · doi:10.1016/j.cortex.2018.04.006 · Cited by 33
Accrual of functional redundancy along the lifespan and its effects on cognition
Sadiq, M. U., Langella, S., Giovanello, K. S., Mucha, P. J., & Dayan, E. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117737 · Cited by 35
Disrupted topology of the resting state structural connectome in middle-aged APOE ε4 carriers
Korthauer, L. E., Zhan, L., Ajilore, O., Leow, A., & Driscoll, I. F. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.052 · Cited by 31
Increased frontal functional networks in adult survivors of childhood brain tumors
Chen, H., Wang, L., King, T. Z., & Mao, H. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.02.010 · Cited by 33
Grajski, K. A., & Bressler, S. L. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101860 · Cited by 33
Vulnerability of multiple large‐scale brain networks in dementia with Lewy bodies
Sala, A., Caminiti, S. P., Iaccarino, L., Beretta, L., Iannaccone, S., Magnani, G., Padovani, A., Ferini‐Strambi, L., & Perani, D. (2019) · Human Brain Mapping · doi:10.1002/hbm.24719 · Cited by 30
Jiang, Y., Huang, H., Abner, E. L., Broster, L. S., Jicha, G. A., Schmitt, F. A., Kryscio, R. J., Andersen, A. H., Powell, D. K., Eldik, L. J. V., et al. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00015 · Cited by 32
Sandrini, M., Manenti, R., Gobbi, E., Rusich, D., Bartl, G., & Cotelli, M. (2019) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2019.107037 · Cited by 31
Foo, H., Thalamuthu, A., Jiang, J., Koch, F. C., Mather, K. A., Wen, W. X., & Sachdev, P. S. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.758817 · Cited by 35
Xie, Y., Liu, T., Ai, J., Chen, D., Zhuo, Y., Zhao, G., Shuai, H., Wu, J., Han, Y., & Yan, T. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00118 · Cited by 30
Longitudinal functional connectivity patterns of the default mode network in healthy older adults
Malagurski, B., Deschwanden, P. F., Jäncke, L., & Mérillat, S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119414 · Cited by 44
Functional Connectivity Changes in Systemic Lupus Erythematosus: A Resting-State Study
Nystedt, J., Mannfolk, P., Jönsen, A., Bengtsson, A. A., Nilsson, P., Sundgren, P. C., & Strandberg, T. O. (2018) · Brain Connectivity · doi:10.1089/brain.2017.0557 · Cited by 29
Chang, Y., Hsu, S., Huang, S., Huang, C., Chang, W., Lien, C.-Y., Lee, J., Lee, C., & Chang, C. (2019) · Alzheimer s Research & Therapy · doi:10.1186/s13195-019-0563-3 · Cited by 29
Tu, M.-C., Hsu, Y., Yang, J., Huang, W., Deng, J. F., Lin, S.-Y., Lin, C.-Y. E., & Kuo, L. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00239 · Cited by 29
Wang, Z., Williams, V. J., Stephens, K. A., Kim, C., Bai, L., Zhang, M., & Salat, D. H. (2020) · Human Brain Mapping · doi:10.1002/hbm.24871 · Cited by 28
Gu, Y., Liu, R., Qin, R., Chen, X., Zou, J., Jiang, Y., Ye, Q., Zhang, B., Bai, F., & Xu, Y. (2018) · Hypertension Research · doi:10.1038/s41440-018-0176-4 · Cited by 28
Spinelli, G., on behalf of the INSIGHT-preAD Study Group, Bakardjian, H., Schwartz, D., Potier, M., Habert, M., Lévy, M., Dubois, B., & George, N. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220204 · Cited by 32
Krajčovičová, L., Bartoň, M., Elfmarková, N., Mikl, M., Mareček, R., & Rektorová, I. (2017) · Journal of Neural Transmission · doi:10.1007/s00702-017-1789-5 · Cited by 28
Funakoshi, Y., Harada, M., Otsuka, H., Mori, K., Ito, H., & Iwanaga, T. (2016) · The Journal of Medical Investigation · doi:10.2152/jmi.63.204 · Cited by 27
Role of retrosplenial cortex in processing stress-related context memories.
Corcoran, K. A., Yamawaki, N., Leaderbrand, K., & Radulović, J. (2018) · Behavioral Neuroscience · doi:10.1037/bne0000223 · Cited by 27
The Effects of Amyloid and Tau on Functional Network Connectivity in Older Populations
Wales, R. M., & Leung, H.-C. (2021) · Brain Connectivity · doi:10.1089/brain.2020.0902 · Cited by 26
The Energetic Collapse of the Alzheimer's Brain: Metabolic Inflexibility Across Cells and Networks
Constantino, N. J., Ashley, C. C., & Macauley, S. L. (2025) · Journal of Neurochemistry · doi:10.1111/jnc.70294 · Cited by 29
Zhao, T., Sheng, C., Bi, Q., Niu, W., Shu, N., & Han, Y. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.08.005 · Cited by 27
Sandrini, M., Manenti, R., Sahin, H., & Cotelli, M. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101065 · Cited by 26
Posterior cortical atrophy phenotypic heterogeneity revealed by decoding 18F-FDG-PET
Townley, R. A., Botha, H., Graff‐Radford, J., Whitwell, J. L., Boeve, B. F., Machulda, M. M., Fields, J. A., Drubach, D. A., Savica, R., Petersen, R. C., et al. (2021) · Brain Communications · doi:10.1093/braincomms/fcab182 · Cited by 25
Self-citationKizilirmak, J. M., Soch, J., Schütze, H., Düzel, E., Feldhoff, H., Fischer, L., Knopf, L., Maaß, A., Raschick, M., Schult, A., et al. (2023) · Human Brain Mapping · doi:10.1002/hbm.26299 · Cited by 23
Generalizing age effects on brain structure and cognition: A two‐study comparison approach
Jockwitz, C., Mérillat, S., Liem, F., Oschwald, J., Amunts, K., Caspers, S., & Jäncke, L. (2019) · Human Brain Mapping · doi:10.1002/hbm.24524 · Cited by 29
Anterior-temporal network hyperconnectivity is key to Alzheimer’s disease: from ageing to dementia
Chauveau, L., Landeau, B., Dautricourt, S., Turpin, A., Delarue, M., Hébert, O., Sayette, V. D. L., Chételat, G., & de Florès, R. (2025) · Brain · doi:10.1093/brain/awaf008 · Cited by 31
In search of multimodal brain alterations in Alzheimer's and Binswanger's disease
Fu, Z., Iraji, A., Caprihan, A., Adair, J. C., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Rosenberg, G. A., & Calhoun, V. D. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101937 · Cited by 25
EEG Resting-State Functional Networks in Amnestic Mild Cognitive Impairment
Caravaglios, G., Muscoso, E. G., Blandino, V., Maria, G. D., Gangitano, M., Graziano, F., Guajana, F., & Piccoli, T. (2022) · Clinical EEG and Neuroscience · doi:10.1177/15500594221110036 · Cited by 38
Zhuang, L., Liu, X., Xu, X., Yue, C., Shu, H., Bai, F., Yu, H., Shi, Y., & Zhang, Z. (2012) · Journal of Neuroinflammation · doi:10.1186/1742-2094-9-263 · Cited by 25
Lee, J. S., Kim, J. H., & Lee, S.-K. (2020) · Psychiatry Investigation · doi:10.30773/pi.2020.0009 · Cited by 25
Bray, N. W., Pieruccini‐Faria, F., Witt, S. T., Bartha, R., Doherty, T. J., Nagamatsu, L. S., Almeida, Q. J., Liu-Ambrose, T. Y. L., Middleton, L. E., Bherer, L., et al. (2023) · GeroScience · doi:10.1007/s11357-023-00805-6 · Cited by 26
Ferreira, L. K., Tamashiro-Duran, J. H., Squarzoni, P., Duran, F., Alves, T. C. T. F., Buchpiguel, C. A., & Busatto, G. F. (2014) · Brazilian Journal of Psychiatry · doi:10.1590/1516-4446-2013-1275 · Cited by 24
Sharma, N., Murari, G., Vandermorris, S., Verhoeff, N. P. L. G., Herrmann, N., Chen, J. J., & Mah, L. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-201579 · Cited by 24
Ballard, H. K., Jackson, T. B., Symm, A. C., Hicks, T. H., & Bernard, J. A. (2022) · Human Brain Mapping · doi:10.1002/hbm.26184 · Cited by 24
Corriveau‐Lecavalier, N., Gunter, J. L., Kamykowski, M. G., Dicks, E., Botha, H., Kremers, W. K., Graff‐Radford, J., Wiepert, D. A., Schwarz, C. G., Yacoub, E. S., et al. (2023) · Brain Communications · doi:10.1093/braincomms/fcad058 · Cited by 23
Self-citationAltomare, D., Benussi, A., Cantoni, V., Premi, E., Rivolta, J., Cupidi, C., Martorana, A., Santarnecchi, E., Padovani, A., Koch, G., et al. (2023) · Alzheimer s Research & Therapy · doi:10.1186/s13195-023-01297-4 · Cited by 24
Impaired value-based decision-making in Parkinson’s disease apathy
Gilmour, W., Mackenzie, G. M., Feile, M., Tayler-Grint, L., Suveges, S., Macfarlane, J. A., Macleod, A. D., Marshall, V. L., Grunwald, I. Q., Steele, J. D., et al. (2024) · Brain · doi:10.1093/brain/awae025 · Cited by 23
Krajčovičová, L., Mikl, M., Mareček, R., & Rektorová, I. (2014) · Journal of Alzheimer s Disease · doi:10.3233/jad-131208 · Cited by 24
Zhang, T., Li, H., Zhang, J., Li, X., Qi, D., Wang, N., & Zhang, Z. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-150810 · Cited by 23
Changes in neurotransmitter-related functional connectivity along the Alzheimer’s disease continuum
Manca, R., Marco, M. D., Soininen, H. S., Ruffini, L., & Venneri, A. (2024) · Brain Communications · doi:10.1093/braincomms/fcaf008 · Cited by 22
Gautherot, M., Kuchcinski, G., Bordier, C., Sillaire, A. R., Delbeuck, X., Leroy, M., Leclerc, X., Pruvo, J., Pasquier, F., & Lopes, R. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.729635 · Cited by 22
Default Mode Network and Neural Phase Synchronization in Healthy Aging: A Resting State EEG Study
Chow, R., Rabi, R., Paracha, S., Hasher, L., Anderson, N. D., & Alain, C. (2022) · Neuroscience · doi:10.1016/j.neuroscience.2022.01.008 · Cited by 23
Cognitive Efficiency in Alzheimer’s Disease is Associated with Increased Occipital Connectivity
Marco, M. D., Duzzi, D., Meneghello, F., & Venneri, A. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-161164 · Cited by 23
Categorizing SHR and WKY rats by chi2 algorithm and decision tree
Tsai, P.-R., Chen, K., Hong, T.-M., Wang, F., & Huang, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-82864-3 · Cited by 21
Guidali, G., Roncoroni, C., & Bolognini, N. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.658723 · Cited by 25
Task-residual functional connectivity of language and attention networks
Tran, S. M., McGregor, K., James, G. A., Gopinath, K., Krishnamurthy, V., Krishnamurthy, L. C., & Crosson, B. (2018) · Brain and Cognition · doi:10.1016/j.bandc.2018.02.003 · Cited by 23
Zhu, Y., Initiative, O. B. O. A. D. N., Gong, L., He, C., Wang, Q., Ren, Q., & Xie, C. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190254 · Cited by 20
β-amyloid monomers drive up neuronal aerobic glycolysis in response to energy stressors
Santangelo, R., Giuffrida, M. L., Satriano, C., Tomasello, M. F., Zimbone, S., & Copani, A. (2021) · Aging · doi:10.18632/aging.203330 · Cited by 21
Huang, H.-M., Chen, G., Liu, Z.-Y., Meng, Q., Li, J., Dong, H., Chen, Y., Zhao, F., Tang, X., Jinliang, G., et al. (2022) · Neural Regeneration Research · doi:10.4103/1673-5374.361530 · Cited by 20
Long-term cognitive training enhances fluid cognition and brain connectivity in individuals with MCI
Gozdas, E., Avelar‐Pereira, B., Fingerhut, H., Dacorro, L., Jo, B., Williams, L. M., O'Hara, R. M., & Hosseini, S. M. H. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03153-x · Cited by 24
Advances in the fMRI analysis of the default mode network: a review
Sanz‐Morales, E., & Melero, H. (2024) · Brain Structure and Function · doi:10.1007/s00429-024-02888-z · Cited by 24
Kulkarni, P., Grant, S. E., Morrison, T. R., Cai, X., Iriah, S. C., Kristal, B. S., Honeycutt, J. A., Brenhouse, H. C., Hartner, J. C., Madularu, D., et al. (2020) · Brain Research · doi:10.1016/j.brainres.2020.147030 · Cited by 19
Viviano, R. P., & Damoiseaux, J. S. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00847-y · Cited by 21
Strain, J. F., Brier, M. R., Tanenbaum, A., Gordon, B. A., McCarthy, J. E., Dincer, A., Marcus, D. S., Chhatwal, J. P., Graff‐Radford, N. R., Day, G. S., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119511 · Cited by 24
Li, Y., Liu, H., Yu, H., Yang, H., Guo, M., Cao, C., Pang, H., Liu, Y., Cao, K., & Fan, G. (2022) · Human Brain Mapping · doi:10.1002/hbm.26058 · Cited by 20
Loss of Parietal Memory Network Integrity in Alzheimer’s Disease
Hu, Y., Du, W., Zhang, Y., Li, N., Han, Y., & Yang, Z. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00067 · Cited by 20
Metadata
- Authors
- 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., Boeve, B. F., Petersen, R. C., & Jack, C. R.
- Affiliations
- Mayo Clinic (US)
- Corresponding author
- Jack, C.R.
- ORCID
- Venue
- Neurology
- Published
- 2011-10-18
- Volume
- 77
- Issue
- 16
- Pages
- 1524-1531
- ISSN
- 0028-3878 (ISSN-L)
- 1526-632X
- PMC
- PMC3198977
- OpenAlex
- W2064066094
- Cited by
- 370
- References
- 30
- Citations by year
- 2026: 11 · 2025: 22 · 2024: 27 · 2023: 20 · 2022: 27 · 2021: 34 · 2020: 24 · 2019: 31 · 2018: 24 · 2017: 26 · 2016: 24 · 2015: 25 · 2014: 29 · 2013: 23 · 2012: 23
- FWCI
- 12.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Neurology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Eli Lilly and Company
- Pfizer
- Baxter International
- Mayo Foundation for Medical Education and Research (AG16574)
- University of Oxford
- Eisai
- National Institutes of Health (AG11378, R01 AG11378, AG16574)
- National Institute on Aging (R01AG11378, P50‐AG16574, U01AG06786, AG 16574, U01AG006786, R01AG011378, AG-11378, P50AG016574)
- National Center for Research Resources (C06RR018898)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Keywords
- default mode network, Alzheimer's disease, brain aging, functional connectivity, posterior default mode network, frontal lobe connectivity, APOE ε4, cognitive performance, brain atrophy, accelerated aging
- MeSH terms
- Age Factors, Aged, Aged, 80 and over, Aging, Alzheimer Disease, Brain, Brain Mapping, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, Neural Pathways, Neuropsychological Tests, Oxygen, Psychiatric Status Rating Scales, Cohort Studies, Statistics, Nonparametric
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
