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Age-related alterations in default mode network: Impact on working memory performance
Open accessAbstract
The default mode network (DMN) is a set of functionally connected brain regions which shows deactivation (task-induced deactivation, TID) during a cognitive task. Evidence shows an age-related decline in task-load-related modulation of the activity within the DMN during cognitive tasks. However, the effect of age on the functional coupling within the DMN and their relation to cognitive performance has hitherto been unexplored. Using functional magnetic resonance imaging, we investigated functional connectivity within the DMN in older and younger subjects during a working memory task with increasing task load. Older adults showed decreased connectivity and ability to suppress low frequency oscillations of the DMN. Additionally, the strength of the functional coupling of posterior cingulate (pCC) with medial prefrontal cortex (PFC) correlated positively with performance and was lower in older adults. pCC was also negatively coupled with task-related regions, namely the dorsolateral PFC and cingulate regions. Our results show that in addition to changes in canonical task-related brain regions, normal aging is also associated with alterations in the activity and connectivity of brain regions within the DMN. These changes may be a reflection of a deficit in cognitive control associated with advancing age that results in deficient resource allocation to the task at hand.
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“Similar to our findings, several previous studies found linear-age-related FCS decreases in the medial prefrontal cortex, precuneus, insula, and calcarine and linear FCS increases in sensorimotor areas based on the general linear model [36-38].”
From Mapping neurophysiological subtypes of major depressive disorder using normative models of the functional connectome (2023) · cites this paper as [37]
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Citations
Cites
The assessment and analysis of handedness: The Edinburgh inventory
Oldfield, R. C. (1971) · Neuropsychologia · doi:10.1016/0028-3932(71)90067-4 · Cited by 35,900
Welch, P. D. (1967) · IEEE Transactions on Audio and Electroacoustics · doi:10.1109/tau.1967.1161901 · Cited by 12,363
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
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
Statistical parametric maps in functional imaging: A general linear approach
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. ‐., Frith, C. D., & Frackowiak, R. S. J. (1994) · Human Brain Mapping · doi:10.1002/hbm.460020402 · Cited by 9,688
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
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
The Psychology of Learning and Motivation: Advances in Research and Theory
(2010) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/c2009-0-62209-1 · Cited by 6,035
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
Working memory, comprehension, and aging: A review and a new view
Hasher, L., & Zacks, R. T. (1988) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(08)60041-9 · Cited by 3,456
Research works printed but not linked: 2
- Greicius, M.D., Supekar, K., Dougherty, R., 2007. Functional connectivity reflects structural connectivity in a human memory network. Proceedings of the Thirteenth Annual Meeting of the Organization for Human Brain Mapping (HBM’07), CD-ROM paper no. 336 TH-PM., 94 W-PM.
- Hugenschmidt, C.E., Peiffer, A.M., Casanova, R., Maldjian, J.A., Burdette, J.H., Hayasaka, S., Laurienti, P.J., 2007. Preservation of default mode functioning in healthy aging adults. Proceedings of the Thirteenth Annual Meeting of the Organization for Human Brain Mapping (HBM’07) CD-ROM paper no. 336 TH-PM., 49 TH-AM.
Cited by
Sorted by citations
Items 201–300 of 516
Zhou, B., Dou, X., Wang, W., Yao, H., Feng, F., Wang, P., Yang, Z., An, N., Liu, B., Zhang, X., et al. (2022) · Methods · doi:10.1016/j.ymeth.2022.06.001 · Cited by 39
Abnormal Activity in the Precuneus during Time Perception in Parkinson's Disease: An fMRI Study
Dušek, P., Jech, R., Sieger, T., Vymazal, J., Růžička, E., Wackermann, J., & Mueller, K. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029635 · Cited by 39
Intrinsic Brain Connectivity Related to Age in Young and Middle Aged Adults
Hampson, M., Tokoglu, F., Shen, X., Scheinost, D., Papademetris, X., & Constable, R. T. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044067 · Cited by 42
Soch, J., Deserno, L., Assmann, A., Barman, A., Walter, H., Richardson‐Klavehn, A., & Schott, B. H. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw206 · Cited by 40
Daneault, V., Dumont, M., Massé, É., Forcier, P., Boré, A., Lina, J., Doyon, J., Vandewalle, G., & Carrier, J. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01557 · Cited by 39
Rieck, J. R., Baracchini, G., Nichol, D., Abdi, H., & Grady, C. L. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.03.019 · Cited by 40
Brain functional connectivity changes in children that differ in impulsivity temperamental trait
Inuggi, A., Sanz‐Arigita, E., González-Salinas, C., Valero-García, A. V., GarcÃa-Santos, J. M., & Fuentes, L. J. (2014) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2014.00156 · Cited by 40
The association between stress and mood across the adult lifespan on default mode network
Soares, J. M., Marques, P., Magalhães, R. J. D. S., Santos, N. C., & Sousa, N. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1203-3 · Cited by 37
Do resting brain dynamics predict oddball evoked-potential?
Lee, T., Yu, Y. W. ‐., Wu, H., & Chen, T. (2011) · BMC Neuroscience · doi:10.1186/1471-2202-12-121 · Cited by 36
The impact of age on prefrontal cortex integrity during spatial working memory retrieval
Toepper, M., Markowitsch, H. J., Gebhardt, H., Beblo, T., Bauer, E., Woermann, F. G., Drießen, M., & Sammer, G. (2014) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2014.04.020 · Cited by 39
Structural connectivity of the default mode network and cognition in Alzheimer׳s disease
Weiler, M., de Campos, B. M., Nogueira, M. H., Damasceno, B. P., Cendes, F., & Balthazar, M. L. F. (2014) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2014.04.008 · Cited by 37
Normal aging modulates prefrontoparietal networks underlying multiple memory processes
Sambataro, F., Safrin, M., Lemaître, H., Steele, S. U., Das, S. B., Callicott, J. H., Weinberger, D. R., & Mattay, V. S. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08254.x · Cited by 37
Ritchay, M. M., Huggins, A. A., Wallace, A. L., Larson, C. L., & Lisdahl, K. M. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102664 · Cited by 36
van Ruitenbeek, P., Monteiro, T. S., Chalavi, S., King, B. R., Cuypers, K., Sunaert, S. G., Peeters, R. R., & Swinnen, S. P. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac514 · Cited by 36
Lowe, A. J., Paquola, C., de Wael, R. V., Girn, M., Larivière, S., Tavakol, S., Caldairou, B., Royer, J., Schrader, D. V., Bernasconi, A., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24767 · Cited by 30
Takeuchi, H., Taki, Y., Nouchi, R., Yokoyama, R., Kotozaki, Y., Nakagawa, S., Sekiguchi, A., Iizuka, K., Hanawa, S., Araki, T., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116825 · Cited by 36
Aging, brain plasticity, and motor learning
Gooijers, J., Pauwels, L., Hehl, M., Seer, C., Cuypers, K., & Swinnen, S. P. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102569 · Cited by 45
Zhou, X., Wu, T., Yu, J., & Lei, X. (2016) · Brain Connectivity · doi:10.1089/brain.2016.0452 · Cited by 37
Functional Alteration of the DMN by Learned Regulation of the PCC Using Real-Time fMRI
Zhang, G., Zhang, H., Li, X., Zhao, X., Yao, L., & Long, Z. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2221480 · Cited by 34
Stimulating aged brains with transcranial direct current stimulation: Opportunities and challenges
Habich, A., Fehér, K. D., Antonenko, D., Boraxbekk, C., Flöel, A., Nissen, C., Siebner, H. R., Thielscher, A., & Klöppel, S. (2020) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2020.111179 · Cited by 34
Zhang, L., Wang, X., Alain, C., & Du, Y. (2023) · Science Advances · doi:10.1126/sciadv.adg7056 · Cited by 36
Chen, Y., Tang, J. H., Chen, Y., Farrand, J., Craft, M., Carlson, B. W., & Yuan, H. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.560878 · Cited by 33
Correas, Á., Holguı́n, S. R., Cuesta, P., López‐Caneda, E., García‐Moreno, L. M., Cadaveira, F., & Maestú, F. (2015) · International Journal of Neural Systems · doi:10.1142/s0129065715500082 · Cited by 32
Brain Connectivity and Cognitive Flexibility in Nonirradiated Adult Survivors of Childhood Leukemia
Billiet, T., Elens, I., Sleurs, C., Uyttebroeck, A., D’Hooge, R., Lemiere, J., & Deprez, S. (2018) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djy009 · Cited by 34
Neural antagonistic mechanism between default-mode and task-positive networks
Cheng, X., Yuan, Y., Wang, Y., & Wang, R. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.079 · Cited by 39
Neuroaging through the Lens of the Resting State Networks
Cieri, F., & Esposito, R. (2018) · BioMed Research International · doi:10.1155/2018/5080981 · Cited by 32
Putcha, D., O’Keefe, K., LaViolette, P. S., O’Brien, J., Greve, D. N., Rentz, D. M., Locascio, J. J., Atri, A., & Sperling, R. A. (2011) · Human Brain Mapping · doi:10.1002/hbm.21166 · Cited by 33
Default Network and Intelligence Difference
Song, M., Liu, Y., Zhou, Y., Wang, K., Yu, C., & Jiang, T. (2009) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2009.2029312 · Cited by 35
Dimitriadis, S. I., Tarnanas, I., Wiederhold, M. D., Wiederhold, B. K., Tsolaki, M., & Fleisch, E. (2016) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1016/j.trci.2016.08.004 · Cited by 31
Executive Functions and Neurocognitive Aging
Spreng, R. N., Shoemaker, L. N., & Turner, G. R. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-803676-1.00008-8 · Cited by 33
Huang, A., Klein, D. N., & Leung, H. (2015) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2015.10.007 · Cited by 35
Ge, R., Fu, Y. H. H., Wang, D., Yao, L., & Long, Z. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00629 · Cited by 29
Sambataro, F., Thomann, P. A., Nolte, H. M., Hasenkamp, J. H., Hirjak, D., Kubera, K. M., Hofer, S., Seidl, U., Depping, M. S., Stieltjes, B., et al. (2019) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2019.06.002 · Cited by 30
Hrybouski, S., Cribben, I., McGonigle, J., Olsen, F., Carter, R., Seres, P., Madan, C. R., & Malykhin, N. V. (2021) · Brain Structure and Function · doi:10.1007/s00429-021-02226-7 · Cited by 28
Zhang, Q., Zhang, G., Li, Y., & Zhao, X. (2015) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2015.00244 · Cited by 32
Pletzer, B. A., Kronbichler, M., Ladurner, G., Nuerk, H., & Kerschbaum, H. (2011) · Brain Research · doi:10.1016/j.brainres.2011.08.058 · Cited by 29
Differences in dynamic and static functional connectivity between young and elderly healthy adults
Park, J. E., Jung, S. C., Ryu, K. H., Oh, J. Y., Kim, H. S., Choi, C.-G., Kim, S. J., & Shim, W. H. (2017) · Neuroradiology · doi:10.1007/s00234-017-1875-2 · Cited by 29
Pauwels, L., Chalavi, S., Gooijers, J., Maes, C., Albouy, G., Sunaert, S. G., & Swinnen, S. P. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2640-17.2018 · Cited by 29
Up-regulation of DMN Connectivity in Mild Cognitive Impairment Via Network-based Cognitive Training
Marco, M. D., Meneghello, F., Pilosio, C., Rigon, J., & Venneri, A. (2017) · Current Alzheimer Research · doi:10.2174/1567205015666171212103323 · Cited by 29
Pinal, D., Zurrón, M., Dı́az, F., & Sauseng, P. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2015.01.009 · Cited by 29
Li, J., Lu, Z., Suo, X., Li, N., Lei, D., Wang, L., Peng, J., Duan, L., Xi, J., Jiang, Y., et al. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-019-00214-4 · Cited by 28
Changes in patterns of age-related network connectivity are associated with risk for schizophrenia
Passiatore, R., Antonucci, L. A., DeRamus, T. P., Fazio, L., Stolfa, G., Sportelli, L., Kikidis, G. C., Blasi, G., Chen, Q., Dukart, J., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2221533120 · Cited by 27
The relation between structural and functional connectivity depends on age and on task goals
Ford, J. H., & Kensinger, E. A. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00307 · Cited by 31
Park, H., Chun, J., Park, B., Park, H., Kim, J. I., Lee, J. D., & Kim, J. (2011) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617711000051 · Cited by 32
Cortical mechanisms of the symptomatology in major depressive disorder: A resting EEG study
Lee, T., Yu, Y. W. ‐., Chen, M., & Chen, T. (2011) · Journal of Affective Disorders · doi:10.1016/j.jad.2010.12.015 · Cited by 28
BOLD Frequency Power Indexes Working Memory Performance
Balsters, J. H., Robertson, I. H., & Calhoun, V. D. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00207 · Cited by 27
Predicting visual working memory with multimodal magnetic resonance imaging
Xiao, Y., Lin, Y., Ma, J., Qian, J., Ke, Z., Li, L., Yi, Y., Zhang, J., Cam‐CAN, & Dai, Z. (2020) · Human Brain Mapping · doi:10.1002/hbm.25305 · Cited by 29
Biophysical mechanism of the interaction between default mode network and working memory network
Yuan, Y., Pan, X., & Wang, R. (2021) · Cognitive Neurodynamics · doi:10.1007/s11571-021-09674-1 · Cited by 26
Brain Networks Reorganization During Maturation and Healthy Aging-Emphases for Resilience
González‐Escamilla, G., Muthuraman, M., Chirumamilla, V. C., Vogt, J., & Groppa, S. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00601 · Cited by 28
Functional brain modules reconfigure at multiple scales across the human lifespanPreprint
Betzel, R. F., Mišić, B., He, Y., Rumschlag, J. A., Zuo, X., & Sporns, O. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.08045 · Cited by 35
Son, J. J., Schantell, M. D., Picci, G., Wang, Y., Stephen, J. M., Calhoun, V. D., Doucet, G. E., Taylor, B. K., & Wilson, T. W. (2023) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2023.101216 · Cited by 29
Kizilirmak, 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
Brown, C. A., Hakun, J. G., Zhu, Z., Johnson, N. F., & Gold, B. T. (2015) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00194 · Cited by 26
Trojsi, F., Monsurrò, M. R., Esposito, F., & Tedeschi, G. (2012) · Neural Plasticity · doi:10.1155/2012/473538 · Cited by 25
Age-related early/late variations of functional connectivity across the human lifespan
Chen, Y., Zhao, X. W., Zhang, X., Liu, Y., Zhou, P., Ni, H., Ma, J., & Ming, D. (2018) · Neuroradiology · doi:10.1007/s00234-017-1973-1 · Cited by 26
Albertson, A. J., Landsness, E. C., Tang, M., Yan, P., Miao, H., Rosenthal, Z. P., Kim, B., Culver, J. P., Bauer, A. Q., & Lee, J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119287 · Cited by 23
Dedifferentiation of emotion regulation strategies in the aging brain
Martins-Klein, B., Ponzio, A., Velasco, R., Kaplan, J., & Mather, M. (2014) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsu129 · Cited by 25
Manelis, A., & Reder, L. M. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00593 · Cited by 28
Quantifying person-level brain network functioning to facilitate clinical translation
Ball, T. M., Goldstein‐Piekarski, A. N., Gatt, J. M., & Williams, L. M. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.204 · Cited by 25
Santhanam, P., Wilson, S. H., Oakes, T. R., & Weaver, L. K. (2019) · Brain Research · doi:10.1016/j.brainres.2019.01.015 · Cited by 29
Li, M., Bian, X., Zhang, J., Wang, Z., & Ma, L. (2020) · Neuroscience Letters · doi:10.1016/j.neulet.2020.135507 · Cited by 23
The effects of psychiatric history and age on self-regulation of the default mode network
Skouras, S., & Scharnowski, F. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.008 · Cited by 24
Ferré, P., Jarret, J., Brambati, S. M., Bellec, P., & Joanette, Y. (2020) · Neurobiology of Language · doi:10.1162/nol_a_00007 · Cited by 25
Scheller, E., Peter, J., Schumacher, L. V., Lahr, J., Mader, I., Kaller, C. P., & Klöppel, S. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.04.015 · Cited by 24
Variation in longitudinal trajectories of cortical sulci in normal elderly
Shen, X., Liu, T., Tao, D., Fan, Y., Zhang, J., Li, S., Jiang, J., Zhu, W., Wang, Y., Wang, Y., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.10.010 · Cited by 24
Rashid, B., Dev, S. I., Esterman, M. S., Schwarz, N. F., Ferland, T., Fortenbaugh, F. C., Milberg, W., McGlinchey, R. E., Salat, D. H., & Leritz, E. C. (2019) · Brain and Behavior · doi:10.1002/brb3.1333 · Cited by 24
Hakun, J. G., & Johnson, N. F. (2017) · Brain and Cognition · doi:10.1016/j.bandc.2017.08.007 · Cited by 23
Age differences in functional network reconfiguration with working memory training
Iordan, A. D., Moored, K., Katz, B., Cooke, K. A., Buschkuehl, M., Jaeggi, S. M., Polk, T. A., Peltier, S., Jonides, J., & Reuter‐Lorenz, P. A. (2021) · Human Brain Mapping · doi:10.1002/hbm.25337 · Cited by 26
Hampel, H. J., Prvulovic, D., Teipel, S. J., & Bokde, A. L. W. (2011) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2011.05.012 · Cited by 24
Marsolais, Y., Perlbarg, V., Benali, H., & Joanette, Y. (2014) · Cortex · doi:10.1016/j.cortex.2014.05.007 · Cited by 25
LORETA Neurofeedback in the Precuneus
Cannon, R. L., Baldwin, D. R., Diloreto, D. J., Phillips, S. T., Shaw, T. L., & Levy, J. J. (2014) · Clinical EEG and Neuroscience · doi:10.1177/1550059413512796 · Cited by 23
Allegra, A., Innao, V., Basile, G., Pugliese, M., Allegra, A. G., Pulvirenti, N., & Musolino, C. (2020) · Expert Review of Hematology · doi:10.1080/17474086.2020.1738213 · Cited by 23
The influence of serotonin transporter polymorphisms on cortical activity: A resting EEG study
Lee, T., Yu, Y. W. ‐., Hong, C., Tsai, S., Wu, H., & Chen, T. (2011) · BMC Neuroscience · doi:10.1186/1471-2202-12-33 · Cited by 23
Aging, neurocognitive reserve, and the healthy brain
Huang, C., & Huang, H.-W. (2019) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/bs.plm.2019.07.006 · Cited by 22
Temporal Dynamics of Functional Brain States Underlie Cognitive Performance
Gu, H., Schulz, K. P., Fan, J., & Yang, Y. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa350 · Cited by 21
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
Korkki, S. M., Richter, F. R., Gellersen, H. M., & Simons, J. S. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2023.04.009 · Cited by 22
Evers, E. A. T., Klaassen, E. B., Rombouts, S. A. R. B., Backes, W. H., & Jolles, J. (2012) · Brain Connectivity · doi:10.1089/brain.2011.0060 · Cited by 23
Arenivas, A., Diaz-Arrastia, R. R., Spence, J. S., Cullum, C. M., Krishnan, K., Bosworth, C. C., Culver, C., Kennard, B. D., & de la Plata, C. M. (2012) · Brain Imaging and Behavior · doi:10.1007/s11682-012-9191-2 · Cited by 23
Inputs to prefrontal cortex support visual recognition in the aging brain
Gilbert, J. R., & Moran, R. (2016) · Scientific Reports · doi:10.1038/srep31943 · Cited by 30
Reduced semantic control in older adults is linked to intrinsic DMN connectivity
Krieger‐Redwood, K., Wang, H.-T., Poerio, G. L., Martinon, L. M., Riby, L. M., Smallwood, J., & Jefferies, E. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.107133 · Cited by 20
Dørum, E. S., Kaufmann, T., Alnæs, D., Andreassen, O. A., Richard, G., Kolskår, K. K., Nordvik, J. E., & Westlye, L. T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.048 · Cited by 20
Takeuchi, H., Taki, Y., Nouchi, R., Yokoyama, R., Kotozaki, Y., Nakagawa, S., Sekiguchi, A., Iizuka, K., Hanawa, S., Araki, T., et al. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1747-5 · Cited by 23
Bak, Y., Nah, Y., Han, S., Lee, S., & Shin, N. (2020) · Scientific Reports · doi:10.1038/s41598-020-66058-x · Cited by 20
La, C., Nair, V. A., Mossahebi, P., Young, B. M., Chacon, M. R., Jensen, M. B., Birn, R. M., Meyerand, M. E., & Prabhakaran, V. (2016) · Brain Connectivity · doi:10.1089/brain.2015.0375 · Cited by 20
Physiological Aging Influence on Brain Hemodynamic Activity during Task-Switching: A fNIRS Study
Vasta, R., Cutini, S., Cerasa, A., Gramigna, V., Olivadese, G., Arabia, G., & Quattrone, A. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00433 · Cited by 21
Neural correlates of episodic memory in healthy aging and Alzheimer's disease.
Meulenbroek, O. (2010) · Radboud Repository (Radboud University) · Cited by 20
Functional Organization of Auditory and Reward Systems in Aging
Belden, A. G., Quinci, M. A., Geddes, M. R., Donovan, N. J., Hanser, S. B., & Loui, P. (2023) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02028 · Cited by 21
Central Pain Processing in Early-Stage Parkinson's Disease: A Laser Pain fMRI Study
Petschow, C., Scheef, L., Paus, S., Zimmermann, N., Schild, H. H., Klockgether, T., & Boecker, H. (2016) · PLoS ONE · doi:10.1371/journal.pone.0164607 · Cited by 19
Zhang, Z., Yan, T.-J., Wang, Y., Zhang, Q., Zhao, W., Chen, X., Zhai, J., Chen, M., Du, B., Deng, X., et al. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.03.039 · Cited by 19
Mayhew, S. D., Coleman, S. C., Mullinger, K. J., & Can, C. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119081 · Cited by 18
Monteiro, T. S., Adab, H. Z., Chalavi, S., Gooijers, J., King, B. R., Cuypers, K., Mantini, D., & Swinnen, S. P. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa021 · Cited by 17
Delgado‐Gallén, S., Soler, M. D., Albu, S., Pachón‐García, C., Alviarez‐Schulze, V., Sánchez, J. S., Bartrés‐Faz, D., Tormos, J. M., Pascual‐Leone, Á., & Cattaneo, G. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.752623 · Cited by 21
Lin, S. S. ‐., & McDonough, I. M. (2021) · Aging Neuropsychology and Cognition · doi:10.1080/13825585.2021.2021134 · Cited by 19
Planning ahead: Predictable switching recruits task‐active and resting‐state networks
Kurtin, D. L., Oiarbide, G. A., Lorenz, R., Violante, I. R., & Hampshire, A. (2023) · Human Brain Mapping · doi:10.1002/hbm.26430 · Cited by 18
Age differences in the neural processing of semantics, within and beyond the core semantic network
Wu, W., & Hoffman, P. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2023.07.022 · Cited by 18
Schott, B. H., Soch, J., Kizilirmak, J. M., Schütze, H., Assmann, A., Maaß, A., Ziegler, G., Sauvage, M. M., & Richter, A. (2023) · iScience · doi:10.1016/j.isci.2023.107765 · Cited by 16
Longitudinal task-negative network analyses in preclinical Huntington’s disease
Wolf, R. C., Sambataro, F., Vasić, N., Wolf, N. D., Thomann, P. A., Landwehrmeyer, G. B., & Orth, M. (2013) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-013-0447-7 · Cited by 19
Jeon, Y., Kim, B., Kim, J. E., Kim, B. R., Ban, S., Jeong, J. H., Kwon, O., Rhie, S. J., Ahn, C.-W., Kim, J., et al. (2016) · The American Journal of Chinese Medicine · doi:10.1142/s0192415x16500270 · Cited by 18
Fabiani, M., Zimmerman, B., & Gratton, G. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-801371-7.00011-9 · Cited by 20
Metadata
- Authors
- Sambataro, F., Murty, V. P., Callicott, J. H., Tan, H.-Y., Das, S., Weinberger, D. R., & Mattay, V. S.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- Mattay, Venkata S.
- ORCID
- Venue
- Neurobiology of Aging
- Publisher
- Elsevier BV
- Published
- 2010-05
- Volume
- 31
- Issue
- 5
- Pages
- 839-852
- ISSN
- 0197-4580 (ISSN-L)
- PMC
- PMC2842461
- OpenAlex
- W2047342826
- Cited by
- 517
- References
- 91
- Citations by year
- 2026: 11 · 2025: 18 · 2024: 18 · 2023: 32 · 2022: 29 · 2021: 28 · 2020: 32 · 2019: 22 · 2018: 29 · 2017: 36 · 2016: 40 · 2015: 37 · 2014: 37 · 2013: 34 · 2012: 38
- FWCI
- 12.58
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Neurobiology of Aging (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Institutional Research Information System (University of Udine) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Funders
- National Institutes of Health (Z99 MH999999)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Mind wandering and attention
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- default mode network, normal aging, working memory, functional connectivity, task-induced deactivation, posterior cingulate cortex, medial prefrontal cortex, cognitive control, workload, low-frequency oscillations, older adults
- MeSH terms
- Adult, Age Factors, Aged, Aged, 80 and over, Aging, Analysis of Variance, Brain Mapping, Female, Gyrus Cinguli, Humans, Image Processing, Computer-Assisted, Functional Laterality, Magnetic Resonance Imaging, Male, Memory, Short-Term, Middle Aged, Nerve Net, Neural Pathways, Neuropsychological Tests, Psychomotor Performance, Reaction Time, Chi-Square Distribution, Prefrontal Cortex
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