Abstract
Recent work in neuroscience supports the idea that variability in brain function is necessary for optimal brain responsivity to a changing environment. In this review, we discuss a series of functional magnetic resonance imaging (fMRI) studies in younger and older adults to assess age-related differences in variability of the fMRI signal. This work shows that moment-to-moment brain signal variability represents an important “signal” within what is typically considered measurement-related “noise” in fMRI. This accumulation of evidence suggests that moving beyond the mean will provide a complementary window into aging-related neural processes.
Similar content being viewed by others
Notes
We also found a similar pattern of relations between positive signal variability and cognitive performance when we analyzed the two age groups separately.
References
Aguirre, G. K., Zarahn, E., & D’Esposito, M. (1998). The variability of human, BOLD hemodynamic responses. NeuroImage, 8(4), 360–369.
Andrews-Hanna, J. R., Snyder, A. Z., Vincent, J. L., Lustig, C., Head, D., Raichle, M. E., et al. (2007). Disruption of large-scale brain systems in advanced aging. Neuron, 56(5), 924–935.
Backman, L., Nyberg, L., Lindenberger, U., Li, S. C., & Farde, L. (2006). The correlative triad among aging, dopamine, and cognition: current status and future prospects. Neuroscience and Biobehavioral Reviews, 30(6), 791–807.
Bandettini, P. A. (2012). Functional MRI: a confluence of fortunate circumstances. NeuroImage, 61(2), A3–A11.
Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., et al. (2008). Probabilistic population codes for Bayesian decision making. Neuron, 60(6), 1142–1152.
Birn, R. M. (2012). The role of physiological noise in resting-state functional connectivity. NeuroImage, 62(2), 864–870.
Cremer, R., & Zeef, E. J. (1987). What kind of noise increases with age? Journal of Gerontology, 42, 515–518.
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011). Emerging concepts for the dynamical organization of resting-state activity in the brain. Nature reviews. Neuroscience, 12(1), 43–56.
Deco, G., Jirsa, V., McIntosh, A. R., Sporns, O., & Kotter, R. (2009). Key role of coupling, delay, and noise in resting brain fluctuations. Proceedings of the National Academy of Sciences of the United States of America, 106(25), 10302–10307.
D’Esposito, M., Deouell, L. Y., & Gazzaley, A. (2003). Alterations in the BOLD fMRI signal with ageing and disease: a challenge for neuroimaging. Nature Reviews Neuroscience, 4(11), 863–872.
Faisal, A. A., Selen, L. P., & Wolpert, D. M. (2008). Noise in the nervous system. Nature Reviews Neuroscience, 9(4), 292–303.
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Science U S A, 102(27), 9673–9678.
Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2010). Blood oxygen level-dependent signal variability is more than just noise. Journal of Neuroscience, 30, 4914–4921.
Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2011). The importance of being variable. Journal of Neuroscience, 31, 4496–4503.
Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2013a). The modulation of BOLD variability between cognitive states varies by age and processing speed. Cerebral Cortex, 23(3), 684–693.
Garrett, D. D., McIntosh, A. R., & Grady, C. L. (2013b). Brain signal variability is parametrically modifiable. Cerebral Cortex. doi:10.1093/cercor/bht150.
Garrett, D. D., Samanez-Larkin, G. R., MacDonald, S. W. S., McIntosh, A. R., & Grady, C. L. (2013c). Moment-to-moment brain variability: a next frontier in human brain mapping? Neuroscience and Biobehavioral Reviews, 37(4), 610–624.
Ghosh, A., Rho, Y., McIntosh, A. R., Kotter, R., & Jirsa, V. K. (2008). Noise during rest enables the exploration of the brain's dynamic repertoire. PLoS computational biology, 4(10), e1000196.
Grady, C. L., Protzner, A. B., Kovacevic, N., Strother, S. C., Afshin-Pour, B., Wojtowicz, M., et al. (2010). A multivariate analysis of age-related differences in default mode and task-positive networks across multiple cognitive domains. Cerebral Cortex, 20(6), 1432–1447.
Handwerker, D. A., Gazzaley, A., Inglis, B. A., & D’Esposito, M. (2007). Reducing vascular variability of fMRI data across aging populations using a breathholding task. Human Brain Mapping, 28, 846–859.
He, B. J. (2011). Scale-free properties of the functional magnetic resonance imaging signal during rest and task. Journal of Neuroscience, 31(39), 13786–13795.
Huettel, S. A., Singerman, J. D., & McCarthy, G. (2001). The effects of aging upon the hemodynamic response measured by functional MRI. Neuro Image, 13(1), 161–175.
Huettel, S. A., Song, A. W., & McCarthy, G. (2004). Functional magnetic resonance imaging. Sunderland: Sinauer Associates.
Jones, T. B., Bandettini, P. A., & Birn, R. M. (2008). Integration of motion correction and physiological noise regression in fMRI. Neuro Image, 42(2), 582–590.
Kannurpatti, S. S., Motes, M. A., Rypma, B., & Biswal, B. B. (2010a). Increasing measurement accuracy of age-related BOLD signal change: minimizing vascular contributions by resting-state-fluctuation-of-amplitude scaling. Human Brain Mapping, 32(7), 1125–1140.
Kannurpatti, S. S., Motes, M. A., Rypma, B., & Biswal, B. B. (2010b). Neural and vascular variability and the fMRI-BOLD response in normal aging. Magnetic Resonance Imaging, 28(4), 466–476.
Knill, D. C., & Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and computation. Trends in Neurosciences, 27(12), 712–719.
Krishnan, A., Williams, L. J., McIntosh, A. R., & Abdi, H. (2011). Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review. Neuro Image, 56(2), 455–475.
Li, S. C., Lindenberger, U., & Sikstrom, S. (2001). Aging cognition: from neuromodulation to representation. Trends in Cognitive Sciences, 5(11), 479–486.
Liu, P., Hebrank, A. C., Rodrigue, K. M., Kennedy, K. M., Section, J., Park, D. C., et al. (2013). Age-related differences in memory-encoding fMRI responses after accounting for decline in vascular reactivity. Neuro Image, 78, 415–425.
Lustig, C., Snyder, A. Z., Bhakta, M., O’Brien, K. C., McAvoy, M., Raichle, M. E., et al. (2003). Functional deactivations: change with age and dementia of the Alzheimer type. Proceedings of the National Academy of Science U S A, 100(24), 14504–14509.
Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006). Bayesian inference with probabilistic population codes. Nature Neuroscience, 9(11), 1432–1438.
MacDonald, S. W., Li, S. C., & Backman, L. (2009). Neural underpinnings of within-person variability in cognitive functioning. Psychology and Aging, 24(4), 792–808.
MacDonald, S. W., Nyberg, L., & Backman, L. (2006). Intra-individual variability in behavior: links to brain structure, neurotransmission and neuronal activity. Trends in Neurosciences, 29(8), 474–480.
McIntosh, A. R., Kovacevic, N., & Itier, R. J. (2008). Increased brain signal variability accompanies lower behavioral variability in development. PLoS Computational Biology, 4(7), e1000106.
McIntosh, A. R., Kovacevic, N., Lippe, S., Garrett, D. D., Grady, C. L., & Jirsa, V. (2010). The development of a noisy brain. Archives Italiennes de Biologie, 148(3), 323–337.
McIntosh, A. R., & Lobaugh, N. L. (2004). Partial least squates analysis of neuroimaging data: applications and advances. Neuro Image, 23(Supplement 1), S250–S263.
McIntosh, A. R., Vakorin, V., Kovacevic, N., Wang, H., Diaconescu, A., & Protzner, A. B. (2013). Spatiotemporal Dependency of Age-Related Changes in Brain Signal Variability. Cerebral Cortex.
McKiernan, K. A., Kaufman, J. N., Kucera-Thompson, J., & Binder, J. R. (2003). A parametric manipulation of factors affecting task-induced deactivation in functional neuroimaging. Journal of Cognitive Neuroscience, 15(3), 394–408.
Miller, M. B., Van Horn, J. D., Wolford, G. L., Handy, T. C., Valsangkar-Smyth, M., Inati, S., et al. (2002). Extensive individual differences in brain activations associated with episodic retrieval are reliable over time. Journal of Cognitive Neuroscience, 14(8), 1200–1214.
Misic, B., Mills, T., Taylor, M. J., & McIntosh, A. R. (2010). Brain noise is task dependent and region specific. Journal of Neurophysiology, 104(5), 2667–2676.
Misic, B., Vakorin, V. A., Paus, T., & McIntosh, A. R. (2011). Functional embedding predicts the variability of neural activity. Frontiers in Systems Neuroscience, 5, 90.
Neumann, J., Lohmann, G., Zysset, S., & von Cramon, D. Y. (2003). Within-subject variability of BOLD response dynamics. Neuro Image, 19(3), 784–796.
Park, D. C., Polk, T. A., Hebrank, A. C., & Jenkins, L. J. (2010). Age differences in default mode activity on easy and difficult spatial judgment tasks. Frontiers in Human Neuroscience, 3, 10.3389/neuro.3309.3075.2009
Persson, J., Lustig, C., Nelson, J. K., & Reuter-Lorenz, P. A. (2007). Age differences in deactivation: a link to cognitive control? Journal of Cognitive Neuroscience, 19(6), 1021–1032.
Protzner, A. B., Kovacevic, N., Cohn, M., & McAndrews, M. P. (2013). Characterizing functional integrity: intraindividual brain signal variability predicts memory performance in patients with medial temporal lobe epilepsy. Journal of Neuroscience, 33(23), 9855–9865.
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Science U S A, 98(2), 676–682.
Raja Beharelle, A., Kovacevic, N., McIntosh, A. R., & Levine, B. (2012). Brain signal variability relates to stability of behavior after recovery from diffuse brain injury. Neuro Image, 60(2), 1528–1537.
Raz, N., Lindenberger, U., Rodrigue, K. M., Kennedy, K. M., Head, D., Williamson, A., et al. (2005). Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cerebral Cortex, 15(11), 1676–1689.
Salthouse, T. A., & Lichty, W. (1985). Tests of the neural noise hypothesis of age-related cognitive change. Journal of Gerontology, 40, 443–450.
Samanez-Larkin, G. R., Kuhnen, C. M., Yoo, D. J., & Knutson, B. (2010). Variability in nucleus accumbens activity mediates age-related suboptimal financial risk taking. Journal of Neuroscience, 30(4), 1426–1434.
Shulman, G. L., Fiez, J., Corbetta, M., Buckner, R. L., Miezin, F., Raichle, M. E., et al. (1997). Common blood flow changes across visual tasks: decreases in cerebral cortex. Journal of Cognitive Neuroscience, 9(5), 648–663.
Smith, S. M., Beckmann, C. F., Ramnani, N., Woolrich, M. W., Bannister, P. R., Jenkinson, M., et al. (2005). Variability in fMRI: a re-examination of inter-session differences. Human Brain Mapping, 24(3), 248–257.
Stein, R. B., Gossen, E. R., & Jones, K. E. (2005). Neuronal variability: noise or part of the signal? Nature Reviews Neuroscience, 6(5), 389–397.
Toro, R., Fox, P. T., & Paus, T. (2008). Functional coactivation map of the human brain. Cerebral Cortex, 18, 2553–2559.
Vakorin, V. A., Lippe, S., & McIntosh, A. R. (2011). Variability of brain signals processed locally transforms into higher connectivity with brain development. Journal of Neuroscience, 31(17), 6405–6413.
Welford, A. T. (1981). Signal, noise, performance, and age. Human Factors, 23, 97–109.
Acknowledgments
This work was supported by the Canadian Institutes of Health Research (grant #MOP14036). C.L.G. also is supported by the Canada Research Chairs program, the Ontario Research Fund, and the Canadian Foundation for Innovation.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Grady, C.L., Garrett, D.D. Understanding variability in the BOLD signal and why it matters for aging. Brain Imaging and Behavior 8, 274–283 (2014). https://doi.org/10.1007/s11682-013-9253-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11682-013-9253-0