The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Attenuated anticorrelation between the default and dorsal attention networks with aging: evidence from task and rest
Open accessAbstract
Anticorrelation between the default and dorsal attention networks is a central feature of human functional brain organization. Hallmarks of aging include impaired default network modulation and declining medial temporal lobe (MTL) function. However, it remains unclear if this anticorrelation is preserved into older adulthood during task performance, or how this is related to the intrinsic architecture of the brain. We hypothesized that older adults would show reduced within- and increased between-network functional connectivity (FC) across the default and dorsal attention networks. To test this hypothesis, we examined the effects of aging on task-related and intrinsic FC using fMRI during an autobiographical planning task known to engage the default network and during rest, respectively, with young (n=72) and older (n=79) participants. The task-related FC analysis revealed reduced anticorrelation with aging. At rest, there was a robust double dissociation, with older adults showing a pattern of reduced within-network FC, but increased between-network FC, across both networks, relative to young adults. Moreover, older adults showed reduced intrinsic resting-state FC of the MTL with both networks suggesting a fractionation of the MTL memory system in healthy aging. These findings demonstrate age-related dedifferentiation among these competitive large-scale networks during both task and rest, consistent with the idea that age-related changes are associated with a breakdown in the intrinsic functional architecture within and among large-scale brain networks.
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Cites
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610
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
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304
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
A component based noise correction method (CompCor) for BOLD and perfusion based fMRI
Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.042 · Cited by 4,502
Methods to detect, characterize, and remove motion artifact in resting state fMRI
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.048 · Cited by 3,748
Remembering the past to imagine the future: the prospective brain
Schacter, D. L., Addis, D. R., & Buckner, R. L. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2213 · Cited by 2,394 · cited 1×
“(2014) demonstrated that theMTLmemory system is indirectly functionally connected to the default network via the posterior parahippocampal gyrusdthe same MTL default network region investigated in the present studyddemonstrating that this default network node may be the critical connection point linking the MTL memory…”
Self-citationMurphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., & Bandettini, P. A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.09.036 · Cited by 2,387
Addis, D. R., Wong, A. T., & Schacter, D. L. (2007) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2006.10.016 · Cited by 1,949
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Wig, G. S. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.09.006 · Cited by 357
Movies in the magnet: Naturalistic paradigms in developmental functional neuroimaging
Vanderwal, T., Eilbott, J., & Castellanos, F. X. (2019) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.10.004 · Cited by 265
The Shifting Architecture of Cognition and Brain Function in Older Adulthood
Spreng, R. N., & Turner, G. R. (2019) · Perspectives on Psychological Science · doi:10.1177/1745691619827511 · Cited by 256
Self-citationBrain functional connectivity correlates of coping styles
Santarnecchi, E., Sprugnoli, G., Tatti, E., Mencarelli, L., Neri, F., Momi, D., Di Lorenzo, G., Pascual-Leone, A., Rossi, S., & Rossi, A. (2018) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/s13415-018-0583-7 · Cited by 85
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Metadata
- Authors
- Spreng, R. N., Stevens, W. D., Viviano, J. D., & Schacter, D. L.
- Venue
- Neurobiology of Aging
- Publisher
- Elsevier BV
- Published
- 2016-09
- Volume
- 45
- Pages
- 149-160
- ISSN
- 0197-4580 (ISSN-L)
- OpenAlex
- W2416451487
- Cited by
- 233
- References
- 26
- FWCI
- 12.34
- Type
- journal article
- Language
- en
- Open access
- green
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