Brain Imaging and Behavior

, Volume 8, Issue 2, pp 274–283

Understanding variability in the BOLD signal and why it matters for aging

SI: Genetic Neuroimaging in Aging and Age-Related Diseases

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.

Keyword

fMRI BOLD signal Variability Aging Cognition 

References

  1. Aguirre, G. K., Zarahn, E., & D’Esposito, M. (1998). The variability of human, BOLD hemodynamic responses. NeuroImage, 8(4), 360–369.PubMedCrossRefGoogle Scholar
  2. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  3. 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.PubMedCrossRefGoogle Scholar
  4. Bandettini, P. A. (2012). Functional MRI: a confluence of fortunate circumstances. NeuroImage, 61(2), A3–A11.CrossRefGoogle Scholar
  5. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  6. Birn, R. M. (2012). The role of physiological noise in resting-state functional connectivity. NeuroImage, 62(2), 864–870.PubMedCrossRefGoogle Scholar
  7. Cremer, R., & Zeef, E. J. (1987). What kind of noise increases with age? Journal of Gerontology, 42, 515–518.PubMedCrossRefGoogle Scholar
  8. 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.Google Scholar
  9. 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.Google Scholar
  10. 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.PubMedCrossRefGoogle Scholar
  11. Faisal, A. A., Selen, L. P., & Wolpert, D. M. (2008). Noise in the nervous system. Nature Reviews Neuroscience, 9(4), 292–303.PubMedCentralPubMedCrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.PubMedCrossRefGoogle Scholar
  14. Garrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. (2011). The importance of being variable. Journal of Neuroscience, 31, 4496–4503.PubMedCentralPubMedCrossRefGoogle Scholar
  15. 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.PubMedCrossRefGoogle Scholar
  16. Garrett, D. D., McIntosh, A. R., & Grady, C. L. (2013b). Brain signal variability is parametrically modifiable. Cerebral Cortex. doi:10.1093/cercor/bht150.PubMedCentralGoogle Scholar
  17. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  18. 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.Google Scholar
  19. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  20. 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.PubMedCrossRefGoogle Scholar
  21. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  22. 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.PubMedGoogle Scholar
  23. Huettel, S. A., Song, A. W., & McCarthy, G. (2004). Functional magnetic resonance imaging. Sunderland: Sinauer Associates.Google Scholar
  24. 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.PubMedCentralPubMedGoogle Scholar
  25. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  26. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  27. 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.PubMedCrossRefGoogle Scholar
  28. 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.PubMedGoogle Scholar
  29. Li, S. C., Lindenberger, U., & Sikstrom, S. (2001). Aging cognition: from neuromodulation to representation. Trends in Cognitive Sciences, 5(11), 479–486.PubMedCrossRefGoogle Scholar
  30. 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.PubMedGoogle Scholar
  31. 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.CrossRefGoogle Scholar
  32. Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006). Bayesian inference with probabilistic population codes. Nature Neuroscience, 9(11), 1432–1438.PubMedCrossRefGoogle Scholar
  33. 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.PubMedCrossRefGoogle Scholar
  34. 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.PubMedCrossRefGoogle Scholar
  35. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  36. 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.PubMedGoogle Scholar
  37. McIntosh, A. R., & Lobaugh, N. L. (2004). Partial least squates analysis of neuroimaging data: applications and advances. Neuro Image, 23(Supplement 1), S250–S263.PubMedGoogle Scholar
  38. 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.Google Scholar
  39. 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.PubMedCrossRefGoogle Scholar
  40. 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.PubMedCrossRefGoogle Scholar
  41. 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.PubMedCrossRefGoogle Scholar
  42. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  43. Neumann, J., Lohmann, G., Zysset, S., & von Cramon, D. Y. (2003). Within-subject variability of BOLD response dynamics. Neuro Image, 19(3), 784–796.PubMedGoogle Scholar
  44. 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.3309.3075.2009Google Scholar
  45. 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.PubMedCrossRefGoogle Scholar
  46. 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.PubMedCrossRefGoogle Scholar
  47. 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.CrossRefGoogle Scholar
  48. 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.PubMedGoogle Scholar
  49. 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.PubMedCrossRefGoogle Scholar
  50. Salthouse, T. A., & Lichty, W. (1985). Tests of the neural noise hypothesis of age-related cognitive change. Journal of Gerontology, 40, 443–450.PubMedCrossRefGoogle Scholar
  51. 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.PubMedCentralPubMedCrossRefGoogle Scholar
  52. 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.PubMedCrossRefGoogle Scholar
  53. 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.PubMedCrossRefGoogle Scholar
  54. 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.PubMedCrossRefGoogle Scholar
  55. Toro, R., Fox, P. T., & Paus, T. (2008). Functional coactivation map of the human brain. Cerebral Cortex, 18, 2553–2559.PubMedCentralPubMedCrossRefGoogle Scholar
  56. 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.PubMedCrossRefGoogle Scholar
  57. Welford, A. T. (1981). Signal, noise, performance, and age. Human Factors, 23, 97–109.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Rotman Research Institute at BaycrestTorontoCanada
  2. 2.Departments of Psychiatry and PsychologyUniversity of TorontoTorontoCanada
  3. 3.Max Planck Society-University College London Initiative in Computational Psychiatry and Aging Research (ICPAR) Center for Lifespan PsychologyMax Planck Institute for Human DevelopmentLondonUK

Personalised recommendations