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Moment-to-Moment BOLD Signal Variability Reflects Regional Changes in Neural Flexibility across the Lifespan
Open accessAbstract
Variability of neuronal responses is thought to underlie flexible and optimal brain function. Because previous work investigating BOLD signal variability has been conducted within task-based fMRI contexts on adults and older individuals, very little is currently known regarding regional changes in spontaneous BOLD signal variability in the human brain across the lifespan. The current study used resting-state fMRI data from a large sample of male and female human participants covering a wide age range (6–85 years) across two different fMRI acquisition parameters (TR = 0.645 and 1.4 s). Variability in brain regions including a key node of the salience network (anterior insula) increased linearly across the lifespan across datasets. In contrast, variability in most other large-scale networks decreased linearly over the lifespan. These results demonstrate unique lifespan trajectories of BOLD variability related to specific regions of the brain and add to a growing literature demonstrating the importance of identifying normative trajectories of functional brain maturation. SIGNIFICANCE STATEMENT Although brain signal variability has traditionally been considered a source of unwanted noise, recent work demonstrates that variability in brain signals during task performance is related to brain maturation in old age as well as individual differences in behavioral performance. The current results demonstrate that intrinsic fluctuations in resting-state variability exhibit unique maturation trajectories in specific brain regions and systems, particularly those supporting salience detection. These results have implications for investigations of brain development and aging, as well as interpretations of brain function underlying behavioral changes across the lifespan.
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“Across the age range from 6 to 85 years, BOLD signal variability decreases linearly across most of the brain, with the exception of the AI, a critical M-CIN node involved in flexibility, which shows the opposite pattern60 (Fig. 3).”
From Cognitive and behavioural flexibility: Neural mechanisms and clinical considerations (2021) · cites this paper as [60]
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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
Saliency, switching, attention and control: a network model of insula function
Menon, V., & Uddin, L. Q. (2010) · Brain Structure and Function · doi:10.1007/s00429-010-0262-0 · Cited by 5,592
Self-citationBrain development during childhood and adolescence: a longitudinal MRI study
Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., Paus, T., Evans, A. C., & Rapoport, J. L. (1999) · Nature Neuroscience · doi:10.1038/13158 · Cited by 5,552
Consistent resting-state networks across healthy subjects
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326
Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates
Eklund, A., Nichols, T. E., & Knutsson, H. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602413113 · Cited by 3,487
Salience processing and insular cortical function and dysfunction
Uddin, L. Q. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3857 · Cited by 2,083
Self-citationResting-state fMRI in the human connectome project
Smith, S. M., Beckmann, C. F., Andersson, J., Auerbach, E. J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D. A., Griffanti, L., Harms, M. P., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.039 · Cited by 1,502
Griffanti, L., Salimi‐Khorshidi, G., Beckmann, C. F., Auerbach, E. J., Douaud, G., Sexton, C. E., Zsoldos, E., Ebmeier, K. P., Filippini, N., Mackay, C. E., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.034 · Cited by 1,273
Reduced resting-state brain activity in the “default network” in normal aging
Damoiseaux, J. S., Beckmann, C. F., Arigita, E. J. S., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Rombouts, S. A. R. B. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhm207 · Cited by 1,187
Alterations in the BOLD fMRI signal with ageing and disease: a challenge for neuroimaging
D’Esposito, M., Deouell, L. Y., & Gazzaley, A. H. (2003) · Nature reviews. Neuroscience · doi:10.1038/nrn1246 · Cited by 852
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Waschke, L., Kloosterman, N. A., Obleser, J., & Garrett, D. D. (2021) · Neuron · doi:10.1016/j.neuron.2021.01.023 · Cited by 317
Deery, H. A., Paolo, R. D., Moran, C., Egan, G. F., & Jamadar, S. D. (2022) · Psychophysiology · doi:10.1111/psyp.14159 · Cited by 188
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Uddin, L. Q. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.06.003 · Cited by 179
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Liao, W., Chen, H., Li, J., Ji, G., Wu, G., Long, Z., Xu, Q., Duan, X., Cui, Q., & Biswal, B. B. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2904555 · Cited by 143
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Shafiei, G., Zeighami, Y., Clark, C. A., Coull, J. T., Nagano‐Saito, A., Leyton, M., Dagher, A., & Mišić, B. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy264 · Cited by 128
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Jung, Y., & Damoiseaux, J. S. (2023) · Brain · doi:10.1093/brain/awad267 · Cited by 123
Kaiser, R. H., Clegg, R. A., Goer, F., Pechtel, P., Beltzer, M., Vitaliano, G. D., Olson, D. P., Teicher, M. H., & Pizzagalli, D. A. (2017) · Psychological Medicine · doi:10.1017/s0033291717002628 · Cited by 122
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Kupis, L. B., Goodman, Z. T., Kornfeld, S., Hoang, S., Romero, C., Dirks, B., Dehoney, J., Chang, C., Spreng, R. N., Nomi, J. S., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab156 · Cited by 107
Self-citationGrady, C. L., & Garrett, D. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.031 · Cited by 103
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Kebets, V., Favre, P., Houenou, J., Polosan, M., Perroud, N. A., Aubry, J., Ville, D. V. D., & Piguet, C. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01666-3 · Cited by 91
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Cornblath, E. J., Tang, E., Baum, G. L., Moore, T. M., Adebimpe, A., Roalf, D. R., Gur, R. C., Gur, R. E., Pasqualetti, F., Satterthwaite, T. D., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.11.048 · Cited by 84
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Elman, J. A., Panizzon, M. S., Hagler, D. J., Eyler, L. T., Granholm, E. L., Fennema‐Notestine, C., Lyons, M. J., McEvoy, L. K., Franz, C. E., Dale, A. M., et al. (2017) · Cortex · doi:10.1016/j.cortex.2017.09.025 · Cited by 82
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Kumral, D., Şansal, F., Cesnaite, E., Mahjoory, K., Al, E., Gaebler, M., Nikulin, V. V., & Villringer, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116373 · Cited by 85
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Baller, E. B., Valcarcel, A. M., Adebimpe, A., Alexander-Bloch, A., Cui, Z., Gur, R., Gur, R. E., Larsen, B., Linn, K. A., O'Donnell, C., et al. (2021) · bioRxiv · doi:10.1016/j.celrep.2022.110576 · Cited by 65
Alavash, M., Lim, S.-J., Thiel, C. M., Sehm, B., Deserno, L., & Obleser, J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.048 · Cited by 70
Scarapicchia, V., Mazerolle, E. L., Fisk, J. D., Ritchie, L. J., & Gawryluk, J. R. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00039 · Cited by 55
Easson, A. K., & McIntosh, A. R. (2019) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2019.100630 · Cited by 56
Herold, F., Aye, N., Lehmann, N., Taubert, M., & Müller, N. G. (2020) · Brain Sciences · doi:10.3390/brainsci10030175 · Cited by 56
Nomi, J. S., Schettini, E., Voorhies, W. I., Bolt, T., Heller, A. S., & Uddin, L. Q. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00090 · Cited by 51
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Goodale, S. E., Ahmed, N., Zhao, C. K., de Zwart, J. A., Özbay, P. S., Picchioni, D., Duyn, J. H., Englot, D. J., Morgan, V. L., & Chang, C. (2021) · eLife · doi:10.7554/elife.62376 · Cited by 53
Denkova, E., Nomi, J. S., Uddin, L. Q., & Jha, A. P. (2019) · Human Brain Mapping · doi:10.1002/hbm.24721 · Cited by 54
Self-citationDynamic functioning of transient resting‐state coactivation networks in the Human Connectome Project
Janes, A. C., Peechatka, A. L., Frederick, B. B., & Kaiser, R. H. (2019) · Human Brain Mapping · doi:10.1002/hbm.24808 · Cited by 44
Inter-regional BOLD signal variability is an organizational feature of functional brain networks
Baracchini, G., Mišić, B., Setton, R., Mwilambwe-Tshilobo, L., Girn, M., Nomi, J. S., Uddin, L. Q., Turner, G. R., & Spreng, R. N. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118149 · Cited by 44
Self-citationGreater BOLD Variability is Associated With Poorer Cognitive Function in an Adult Lifespan Sample
Boylan, M. A., Foster, C. M., Pongpipat, E. E., Webb, C. E., Rodrigue, K. M., & Kennedy, K. M. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa243 · Cited by 46
Linke, A. C., Mash, L. E., Fong, C. H., Kinnear, M., Kohli, J. S., Wilkinson, M. K., Tung, R., Keehn, R. J. J., Carper, R. A., Fishman, I., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117383 · Cited by 42
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Madan, C. R. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00405 · Cited by 40
Zhang, L., Zuo, X., Ng, K. K., Chong, J. S. X., Shim, H. Y., Ong, M. Q. W., Loke, Y. M., Choo, B. L., Chong, E. J. Y., Wong, Z. X., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-63540-4 · Cited by 44
Barber, A. D., Hegarty, C. E., Lindquist, M. A., & Karlsgodt, K. H. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa391 · Cited by 35
Wainio‐Theberge, S., Wolff, A., Gómez‐Pilar, J., Zhang, J., & Northoff, G. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119245 · Cited by 39
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Bottenhorn, K. L., Cardenas‐Iniguez, C., Mills, K. L., Laird, A. R., & Herting, M. M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120287 · Cited by 40
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Millar, P. R., Ances, B. M., Gordon, B. A., Benzinger, T. L. S., Fagan, A. M., Morris, J. C., & Balota, D. A. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.08.007 · Cited by 30
Garrett, D. D., Skowron, A., Wiegert, S., Adolf, J., Dahle, C. L., Lindenberger, U., & Raz, N. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab154 · Cited by 33
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Zhang, J., Sun, W. W., & Li, L. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2019.1677242 · Cited by 34
Millar, P. R., Petersen, S. E., Ances, B. M., Gordon, B. A., Benzinger, T. L. S., Morris, J. C., & Balota, D. A. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa138 · Cited by 30
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Song, I., Neal, J., & Lee, T. (2021) · Brain Sciences · doi:10.3390/brainsci11111485 · Cited by 28
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Maidan, I., Hacham, R., Galperin, I., Giladi, N., Holtzer, R., Hausdorff, J. M., & Mirelman, A. (2021) · Neurology · doi:10.1212/wnl.0000000000013217 · Cited by 29
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Self-citationStoica, T., & Depue, B. E. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.571070 · Cited by 32
Zöller, D., Padula, M. C., Sandini, C., Schneider, M., Scariati, E., Ville, D. V. D., Schaer, M., & Eliez, S. (2017) · Schizophrenia Research · doi:10.1016/j.schres.2017.08.003 · Cited by 27
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Zhao, J., Liu, H., Li, J., Sun, H., Liu, Z., Gao, J. B., Liu, Y., & Huang, C. (2019) · Scientific Reports · doi:10.1038/s41598-019-55624-7 · Cited by 25
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Self-citationMoreira, J. F. G., McLaughlin, K. A., & Silvers, J. A. (2019) · Developmental Psychology · doi:10.1037/dev0000687 · Cited by 26
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Neural variability in three major psychiatric disorders
Wei, W., Deng, L., Qiao, C., Yin, Y., Zhang, Y., Li, X., Yu, H., Jian, L., Li, M., Guo, W., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02164-2 · Cited by 26
Rolls, E. T., Zhang, R., Deco, G., Vatansever, D., & Feng, J. (2024) · Human Brain Mapping · doi:10.1002/hbm.70056 · Cited by 21
Sheng, J., Zhang, L., Feng, J., Liu, J., Li, A., Chen, W., Shen, Y., Wang, J., Yong He, A., & Xue, G. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118187 · Cited by 22
Good, T. J., Villafuerte, J. W., Ryan, J. D., Grady, C. L., & Barense, M. D. (2020) · eNeuro · doi:10.1523/eneuro.0290-19.2020 · Cited by 22
Scarapicchia, V., García-Barrera, M. A., MacDonald, S. W. S., & Gawryluk, J. R. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00429 · Cited by 21
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Lim, M., Kim, D. J., Nascimento, T. D., Ichesco, E., Kaplan, C. M., Harris, R. E., & DaSilva, A. F. M. (2021) · Neuromodulation Technology at the Neural Interface · doi:10.1111/ner.13512 · Cited by 22
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Wang, Y., Chen, W., Ye, L., Biswal, B. B., Yang, X., Zou, Q., Yang, P., Yang, Q., Wang, X., Cui, Q., et al. (2018) · Human Brain Mapping · doi:10.1002/hbm.23992 · Cited by 20
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Baracchini, G., Zhou, Y., Castanheira, J. D. S., Hansen, J. Y., Rieck, J. R., Turner, G. R., Grady, C. L., Mišić, B., Nomi, J. S., Uddin, L. Q., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.22.563476 · Cited by 18
Self-citationMaillet, D., Yu, L., Lau, B., Chow, R., Alain, C., & Grady, C. L. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2020.107565 · Cited by 17
Millar, P. R., Ances, B. M., Gordon, B. A., Benzinger, T. L. S., Morris, J. C., & Balota, D. A. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01645 · Cited by 15
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Xie, K., Sahlas, E., Ngo, A., Chen, J., Arafat, T., Royer, J., Zhou, Y., Rodríguez‐Cruces, R., Dascal, A., Caldairou, B., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-65042-1 · Cited by 19
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Li, A., Liu, H., Lei, X., He, Y., Wu, Q., Yan, Y., Zhou, X., Tian, X., Peng, Y., Huang, S., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38972-x · Cited by 16
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Seidel, M., Geisler, D., Borchardt, V., King, J. A., Bernardoni, F., Jaite, C., Roessner, V., Calhoun, V. D., Walter, M., & Ehrlich, S. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-01081-0 · Cited by 18
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Metadata
- Authors
- Nomi, J. S., Bolt, T. S., Ezie, C. E. C., Uddin, L. Q., & Heller, A. S.
- Affiliations
- University of Miami (US)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2017-05-04
- Volume
- 37
- Issue
- 22
- Pages
- 5539-5548
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC5452342
- OpenAlex
- W2611008202
- Cited by
- 164
- References
- 38
- Citations by year
- 2026: 16 · 2025: 16 · 2024: 21 · 2023: 17 · 2022: 21 · 2021: 24 · 2020: 20 · 2019: 14 · 2018: 9 · 2017: 6
- FWCI
- 7.60
- 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
- University of Miami
- National Alliance for Research on Schizophrenia and Depression (R01MH107549)
- National Institute of Mental Health (R01MH107549)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- BOLD signal variability, resting-state fMRI, lifespan development, brain maturation, neural flexibility, salience network, anterior insula, large-scale brain networks, aging, individual differences
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Aging, Brain, Brain Mapping, Cerebrovascular Circulation, Child, Female, Humans, Longevity, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, Neuronal Plasticity, Oxygen Consumption, Rest, Young Adult
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