Advertisement

Neuropsychology Review

, Volume 24, Issue 3, pp 267–270 | Cite as

Neuroimaging of the Aging Brain: Introduction to the Special Issue of Neuropsychology Review

  • Samuel Lockhart
  • Charles DeCarli
  • Rosemary FamaEmail author
Editorial

Everyone knows what cognitive aging is, to borrow from William James’ famous definition of attention (James 1890). Cognitive aging is a gradual, late-life decline in cognitive performance, experienced to a degree by most individuals fortunate to reach old age (Grady 2008). Decades of scientific research have shown that there exist age-correlated deficits both in basic cognitive processes (such as speed of processing) and in higher-order cognitive functions, particularly episodic memory (ability to recall events) and cognitive control (ability to control our behaviors). Cognitive aging research also indicates that while some functions decline, others remain relatively intact and may even improve with age (such as semantic knowledge (Grady and Craik 2000; Park and Reuter-Lorenz 2009; Salthouse 2004, 2009)). Still, relatively subtle declines in cognitive performance—for example, in memory and executive functions—are frequently observed in rigorous studies of older adults without clinical conditions. This phenomenon of age-associated cognitive declines, unrelated to detectable clinical processes and distinct from Mild Cognitive Impairment (MCI), is often termed “normal” or “healthy cognitive aging,” and these elders are considered “cognitively normal” or “cognitively healthy older adults.” Even such mild cognitive declines, however, can affect functionally relevant clinical outcomes related to older age including increasing the rate of hospitalization (Wilson et al. 2014) and predicting poorer medication adherence (Hayes et al. 2009).

Brain structural morphology differs with age (DeCarli et al. 2005; Fjell et al. 2013; Head et al. 2008; Raz et al. 1998; Raz et al. 1997, 2005) and accompanies age-related differences in cognition. These differences are regionally specific with the most consistent and notable age-related differences in frontal lobar and medial temporal regions (Buckner 2004; DeCarli et al. 2005; Fjell et al. 2014; Head et al. 2008; Raz et al. 1998). Newer and more technologically sophisticated imaging tools have also identified relevant age-related differences in white matter microstructure which may either underpin gray-matter atrophy differences or accompany such differences (Andrews-Hanna et al. 2007; O’Sullivan et al. 2001; Sullivan et al. 2001; Sullivan and Pfefferbaum 2006, 2007). The notion of age-related dysfunction of specific brain systems has led to the development of a host of theories aimed at integrating these two apparently related processes. The frontal theory of aging, which is consistent with the known age-related differences in frontal gray matter, is one early example (West 1996). Another theory (Salthouse 1988, 1996, 2000; Salthouse and Lichty 1985) invoked the notion of slowed or degraded signal processing, which is finding greater supportive evidence through the study of white matter microstructure. Of course these are only two of the many theories. As our knowledge of cognitive processes expands, more refined hypotheses such as that of separate and dissociable memory processes related to familiarity and recall as discussed in papers by Schoemaker and colleagues and Koen and Yonelinas in this issue (Schoemaker et al. 2014 and Koen and Yonelinas 2014) are being developed. Novel hypotheses incorporate individual differences in cognitive aging through forms of reserve capacity as discussed in this issue by Reuter-Lorenz and Park (Reuter-Lorenz and Park 2014). These relatively recent hypotheses also have recognized discrete anatomical underpinnings that may be further understood by imaging and physiological techniques on the scientific horizon.

Study of the aging brain cannot be fully understood without knowledge of common age-related diseases (DeCarli 2013) and genetic influences (Atwood et al. 2004; Bis et al. 2012; Carmelli et al. 1998; DeStefano et al. 2009; Ikram et al. 2012; Pfefferbaum et al. 2000). Advanced age is associated with multiple overlapping biological processes that adversely affect brain structure and function. Such processes include Alzheimer’s disease (AD) and cerebrovascular disease (CVD), which are nearly equally prevalent with advancing age (Seshadri and Wolf 2007). Moreover, both AD and CVD processes are known to have an extended prodromal state during which an individual appears “normal” (Debette et al. 2011; DeCarli et al. 1995; Jack et al. 2013; Pike et al. 2007; Rowe et al. 2010; Sperling et al. 2011; Swan et al. 1998, 2000). Brain aging refers to imaging signs of age-related changes absent clinically significant cognitive impairment; however, it remains unclear as to what extent these differences are “normal,” and to which biological processes differences in brain structure and function can be attributed. As the brain provides the substrate for cognition, brain aging and cognitive aging are, by definition, closely linked (Grady 2008).

Often age-related biological processes are difficult to disentangle from one another, with the preclinical effects of AD and CVD thought to contribute to and confound the study of normal brain aging (Lockhart et al. 2012, 2014; Mayda et al. 2011). The effects on brain aging of clinically asymptomatic CVD are particularly insidious, as CVD and cardiovascular risk factors are very common (Decarli 2004; Wolf et al. 1991), and vascular risk factor-related brain structural differences (e.g., reduced white matter integrity) are observable as early as mid-life (Maillard et al. 2012). The study of brain aging must therefore examine the contributions of normal and preclinical disease-related changes to the structure and function of the brain.

This issue of Neuropsychology Review presents a series of papers that cogently synthesize and summarize structural and functional brain differences with advancing age including the potential impact of amyloid, cerebrovascular risk factors, and genetic influences on these differences, aspects of cognitive functioning with a specific focus on age-related differences in memory function, and cognitive reserve capacity.

The first section of this issue focuses on biomarkers related to aging. Lockhart and DeCarli provide an overview of structural imaging measures in brain aging including cross-sectional and longitudinal brain imaging studies assessing brain differences in younger versus older adults and the effects of clinically silent CVD risk factors on cognition and behavior in advancing age. Next, Fouquet, Besson, Gonneaud, La Joie, and Chetelat review imaging studies of the effects of ApoE4 on brain structure and function in cognitively normal adults across the lifespan. These authors focus on three main neuroimaging markers associated with AD: cortical beta-amyloid deposition, hypometabolism, and atrophy in Apoe4 carriers versus noncarriers. Factors that influence the association between beta amyloid and cognition in advanced age is next reviewed by Mormino, who makes a strong case for the need of a multimodal approach in determining AD risk.

The second section of this issue focuses on memory processes, specifically recollection and familiarity. Schoemaker, Gautheir, and Pruessner review these memory processes and their neural substrates in older adults with MCI and AD and include remember-know, process dissociation procedure, and receiver operating characteristic paradigms. Next, Koen and Yonelinas provide a meta-analytic review of the effect sizes reported in recollection and familiarity studies in healthy aging, amnestic MCI and AD. Conclusions from both these reviews are consistent with neuroimaging findings suggesting a double dissociation: the hippocampus plays a critical role in recollection, whereas perirhinal regions play a critical role in familiarity.

The last two papers of this issue take a gestalt approach, incorporating what we know about brain and aging to inform interventions to increase level of cognitive functioning and quality of life in older adults. Reuter-Lorenz and Park provide a revision of their previously well-received STAC (Scaffolding Theory of Aging and Cognition) model of cognitive functioning in normal aging. In their conceptual model of cognitive aging, they integrate the influence of biological, environmental, and lifestyle variables and effects level of cognitive functioning and rate of cognitive decline in advancing age. Lastly, the paper by Carmichael reviews what is known and what is not yet known about vascular effects on the brain and cognition in later adulthood. This paper provides strong support for the relevance of therapeutic intervention of vascular risk factors to maintain cognitive health in later life.

Taken together, these timely reviews highlight the need for a better understanding of modifiable factors related to “normal” brain aging so that we may move toward “optimal” brain aging and the retention of strong cognitive abilities even into the ninth decade of life.

Notes

Acknowledgments

During the writing of this work, Dr. Rosemary Fama received support from grant AA017168, and Dr. Charles DeCarli received support from grant P30 AG10129.

References

  1. Andrews-Hanna, J. R., Snyder, A. Z., Vincent, J. L., Lustig, C., Head, D., Raichle, M. E., & Buckner, R. L. (2007). Disruption of large-scale brain systems in advanced aging. Neuron, 56(5), 924–935.PubMedCentralPubMedCrossRefGoogle Scholar
  2. Atwood, L. D., Wolf, P. A., Heard-Costa, N. L., Massaro, J. M., Beiser, A., D’Agostino, R. B., & DeCarli, C. (2004). Genetic variation in white matter hyper intensity volume in the Framingham Study. Stroke, 35(7), 1609–1613.PubMedCrossRefGoogle Scholar
  3. Bis, J. C., Decarli, C., Smith, A. V., van der Lijn, F., Crivello, F., Fornage, M., & Seshadri, S. (2012). Common variants at 12q14 and 12q24 are associated with hippocampal volume. Nature Genetics, 44(5), 545–551. doi: 10.1038/ng.2237.PubMedCentralPubMedCrossRefGoogle Scholar
  4. Buckner, R. L. (2004). Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron, 44(1), 195–208.PubMedCrossRefGoogle Scholar
  5. Carmelli, D., DeCarli, C., Swan, G. E., Jack, L. M., Reed, T., Wolf, P. A., & Miller, B. L. (1998). Evidence for genetic variance in white matter hyper intensity volume in normal elderly male twins. Stroke, 29(6), 1177–1181.PubMedCrossRefGoogle Scholar
  6. Debette, S., Seshadri, S., Beiser, A., Au, R., Himali, J. J., Palumbo, C., & Decarli, C. (2011). Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline. Neurology, 77(5), 461–468. doi: 10.1212/WNL.0b013e318227b227.PubMedCentralPubMedCrossRefGoogle Scholar
  7. Decarli, C. (2004). Vascular factors in dementia: an overview. Journal of Neurological Sciences, 226(1–2), 19–23.CrossRefGoogle Scholar
  8. DeCarli, C. (2013). Clinically asymptomatic vascular brain injury: a potent cause of cognitive impairment among older individuals. Journal of Alzheimer’s Disease, 33(Suppl 1), S417–S426. doi: 10.3233/JAD-2012-129004.PubMedCentralPubMedGoogle Scholar
  9. DeCarli, C., Murphy, D. G., Tranh, M., Grady, C. L., Haxby, J. V., Gillette, J. A., et al. (1995). The effect of white matter hyper intensity volume on brain structure, cognitive performance, and cerebral metabolism of glucose in 51 healthy adults. Neurology, 45(11), 2077–2084.PubMedCrossRefGoogle Scholar
  10. DeCarli, C., Massaro, J., Harvey, D., Hald, J., Tullberg, M., Au, R., & Wolf, P. A. (2005). Measures of brain morphology and infarction in the framingham heart study: establishing what is normal. Neurobiology of Aging, 26(4), 491–510.PubMedCrossRefGoogle Scholar
  11. DeStefano, A. L., Seshadri, S., Beiser, A., Atwood, L. D., Massaro, J. M., Au, R., & DeCarli, C. (2009). Bivariate heritability of total and regional brain volumes: the Framingham Study. Alzheimer Disease and Associated Disorders, 23(3), 218–223.PubMedCentralPubMedCrossRefGoogle Scholar
  12. Fjell, A. M., Westlye, L. T., Grydeland, H., Amlien, I., Espeseth, T., Reinvang, I., & Alzheimer Disease Neuroimaging, I. (2013). Critical ages in the life course of the adult brain: nonlinear subcortical aging. Neurobiology of Aging, 34(10), 2239–2247. doi: 10.1016/j.neurobiolaging.2013.04.006.PubMedCrossRefGoogle Scholar
  13. Fjell, A. M., Westlye, L. T., Grydeland, H., Amlien, I., Espeseth, T., Reinvang, I., & Alzheimer Disease Neuroimaging, I. (2014). Accelerating cortical thinning: unique to dementia or universal in aging? Cerebral Cortex, 24(4), 919–934. doi: 10.1093/cercor/bhs379.PubMedCrossRefGoogle Scholar
  14. Grady, C. L. (2008). Cognitive neuroscience of aging. Annals of the New York Academy of Sciences, 1124, 127–144. doi: 10.1196/annals.1440.009.PubMedCrossRefGoogle Scholar
  15. Grady, C. L., & Craik, F. I. (2000). Changes in memory processing with age. Current Opinion in Neurobiology, 10(2), 224–231.PubMedCrossRefGoogle Scholar
  16. Hayes, T. L., Larimer, N., Adami, A., & Kaye, J. A. (2009). Medication adherence in healthy elders: small cognitive changes make a big difference. Journal of Aging and Health, 21(4), 567–580. doi: 10.1177/0898264309332836.PubMedCentralPubMedCrossRefGoogle Scholar
  17. Head, D., Rodrigue, K. M., Kennedy, K. M., & Raz, N. (2008). Neuroanatomical and cognitive mediators of age-related differences in episodic memory. Neuropsychology, 22(4), 491–507.PubMedCentralPubMedCrossRefGoogle Scholar
  18. Ikram, M. A., Fornage, M., Smith, A. V., Seshadri, S., Schmidt, R., Debette, S., & Wilson, J. F. (2012). Common variants at 6q22 and 17q21 are associated with intracranial volume. Nature Genetics, 44(5), 539–544. doi: 10.1038/ng.2245.PubMedCentralPubMedCrossRefGoogle Scholar
  19. Jack, C. R., Jr., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., & Trojanowski, J. Q. (2013). Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurology, 12(2), 207–216. doi: 10.1016/S1474-4422(12)70291-0.PubMedCentralCrossRefGoogle Scholar
  20. James, W. (1890). Principals of psychology. London: MacMillan and Company.CrossRefGoogle Scholar
  21. Koen, J. D., & Yonelinas, A. P. (2014). The effects of healthy aging, amnestic mild cognitive impairment, and Alzheimer’s disease on recollection and familiarity: a meta-analytic review. Neuropsychology Review, 24(3). doi: 10.1007/s11065-014-9266-5.
  22. Lockhart, S. N., Mayda, A. B., Roach, A. E., Fletcher, E., Carmichael, O., Maillard, P., & Decarli, C. (2012). Episodic memory function is associated with multiple measures of white matter integrity in cognitive aging. Frontiers in Human Neuroscience, 6, 56. doi: 10.3389/fnhum.2012.00056.PubMedCentralPubMedCrossRefGoogle Scholar
  23. Lockhart, S. N., Roach, A. E., Luck, S. J., Geng, J., Beckett, L., Carmichael, O., & DeCarli, C. (2014). White matter hyper intensities are associated with visual search behavior independent of generalized slowing in aging. Neuropsychologia, 52, 93–101. doi: 10.1016/j.neuropsychologia.2013.10.011.PubMedCrossRefGoogle Scholar
  24. Maillard, P., Seshadri, S., Beiser, A., Himali, J. J., Au, R., Fletcher, E., & DeCarli, C. (2012). Effects of systolic blood pressure on white-matter integrity in young adults in the Framingham Heart Study: a cross-sectional study. Lancet Neurology, 11(12), 1039–1047. doi: 10.1016/S1474-4422(12)70241-7.PubMedCentralCrossRefGoogle Scholar
  25. Mayda, A. B., Westphal, A., Carter, C. S., & DeCarli, C. (2011). Late life cognitive control deficits are accentuated by white matter disease burden. Brain, 134(Pt 6), 1673–1683. doi: 10.1093/brain/awr065.PubMedCentralPubMedCrossRefGoogle Scholar
  26. O’Sullivan, M., Jones, D. K., Summers, P. E., Morris, R. G., Williams, S. C. R., & Markus, H. S. (2001). Evidence for cortical “disconnection” as a mechanism of age-related cognitive decline. Neurology, 57(4), 632–638.PubMedCrossRefGoogle Scholar
  27. Park, D. C., & Reuter-Lorenz, P. (2009). The adaptive brain: aging and neurocognitive scaffolding. Annual Review of Psychology, 60, 173–196. doi: 10.1146/annurev.psych.59.103006.093656.PubMedCentralPubMedCrossRefGoogle Scholar
  28. Pfefferbaum, A., Sullivan, E. V., Swan, G. E., & Carmelli, D. (2000). Brain structure in men remains highly heritable in the seventh and eighth decades of life. Neurobiology of Aging, 21(1), 63–74.PubMedCrossRefGoogle Scholar
  29. Pike, K. E., Savage, G., Villemagne, V. L., Ng, S., Moss, S. A., Maruff, P., & Rowe, C. C. (2007). Beta-amyloid imaging and memory in non-demented individuals: evidence for preclinical Alzheimer’s disease. Brain, 130(Pt 11), 2837–2844.PubMedCrossRefGoogle Scholar
  30. Raz, N., Gunning, F. M., Head, D., Dupuis, J. H., McQuain, J., Briggs, S. D., & Acker, J. D. (1997). Selective aging of the human cerebral cortex observed in vivo: differential vulnerability of the prefrontal gray matter. Cerebral Cortex, 7(3), 268–282.PubMedCrossRefGoogle Scholar
  31. Raz, N., Gunning-Dixon, F. M., Head, D., Dupuis, J. H., & Acker, J. D. (1998). Neuroanatomical correlates of cognitive aging: evidence from structural magnetic resonance imaging. Neuropsychology, 12(1), 95–114.PubMedCrossRefGoogle Scholar
  32. Raz, N., Lindenberger, U., Rodrigue, K. M., Kennedy, K. M., Head, D., Williamson, A., & Acker, J. D. (2005). Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cerebral Cortex, 15(11), 1676–1689. doi: 10.1093/cercor/bhi044.PubMedCrossRefGoogle Scholar
  33. Reuter-Lorenz, P. A., & Park, D. C. (2014). How does it STAC up? Revisiting the scaffolding theory of aging and cognition. Neuropsychology Review, 24(3). doi: 10.1007/s11065-014-9270-9.
  34. Rowe, C. C., Ellis, K. A., Rimajova, M., Bourgeat, P., Pike, K. E., Jones, G., & Villemagne, V. L. (2010). Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Neurobiology of Aging, 31(8), 1275–1283. doi: 10.1016/j.neurobiolaging.2010.04.007.PubMedCrossRefGoogle Scholar
  35. Salthouse, T. A. (1988). The role of processing resources in cognitive aging. In M. L. Howe & C. J. Brainerd (Eds.), Cognitive development in adulthood: Progress in cognitive development research (pp. 185–239). New York: Springer.CrossRefGoogle Scholar
  36. Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103(3), 403–428.PubMedCrossRefGoogle Scholar
  37. Salthouse, T. A. (2000). Aging and measures of processing speed. Biological Psychology, 54(1–3), 35–54.PubMedCrossRefGoogle Scholar
  38. Salthouse, T. (2004). What and when of cognitive aging. Current Directions in Psychological Science, 13(4), 140–144.CrossRefGoogle Scholar
  39. Salthouse, T. A. (2009). When does age-related cognitive decline begin? Neurobiology of Aging, 30(4), 507–514. doi: 10.1016/j.neurobiolaging.2008.09.023.PubMedCentralPubMedCrossRefGoogle Scholar
  40. 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
  41. Schoemaker, D., Gauthier, S., & Pruessner, J. C. (2014). Recollection and familiarity in aging individuals with mild cognitive impairment and Alzheimer’s disease: a literature review. Neuropsychology Review, 24(3). doi: 10.1007/s11065-014-9265-6.
  42. Seshadri, S., & Wolf, P. A. (2007). Lifetime risk of stroke and dementia: current concepts, and estimates from the Framingham Study. Lancet Neurology, 6(12), 1106–1114.CrossRefGoogle Scholar
  43. Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., & Phelps, C. H. (2011). Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement, 7(3), 280–292. doi: 10.1016/j.jalz.2011.03.003.PubMedCentralPubMedCrossRefGoogle Scholar
  44. Sullivan, E. V., & Pfefferbaum, A. (2006). Diffusion tensor imaging and aging. Neuroscience and Biobehavioral Reviews, 30(6), 749–761.PubMedCrossRefGoogle Scholar
  45. Sullivan, E. V., & Pfefferbaum, A. (2007). Neuroradiological characterization of normal adult ageing. The British Journal of Radiology, 80(Spec No 2), S99–S108. doi: 10.1259/bjr/22893432.PubMedCrossRefGoogle Scholar
  46. Sullivan, E. V., Adalsteinsson, E., Hedehus, M., Ju, C., Moseley, M., Lim, K. O., & Pfefferbaum, A. (2001). Equivalent disruption of regional white matter microstructure in ageing healthy men and women. Neuroreport, 12(1), 99–104.PubMedCrossRefGoogle Scholar
  47. Swan, G. E., DeCarli, C., Miller, B. L., Reed, T., Wolf, P. A., Jack, L. M., & Carmelli, D. (1998). Association of midlife blood pressure to late-life cognitive decline and brain morphology. Neurology, 51(4), 986–993.PubMedCrossRefGoogle Scholar
  48. Swan, G. E., DeCarli, C., Miller, B. L., Reed, T., Wolf, P. A., & Carmelli, D. (2000). Biobehavioral characteristics of nondemented older adults with subclinical brain atrophy. Neurology, 54(11), 2108–2114.PubMedCrossRefGoogle Scholar
  49. West, R. L. (1996). An application of prefrontal cortex function theory to cognitive aging. Psychological Bulletin, 120(2), 272–292.PubMedCrossRefGoogle Scholar
  50. Wilson, R. S., Rajan, K. B., Barnes, L. L., Hebert, L. E., Mendes de Leon, C. F., & Evans, D. A. (2014). Cognitive aging and rate of hospitalization in an urban population of older people. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 69(4), 447–454. doi: 10.1093/gerona/glt145.CrossRefGoogle Scholar
  51. Wolf, P. A., D’Agostino, R. B., Belanger, A. J., & Kannel, W. B. (1991). Probability of stroke: a risk profile from the Framingham Study. Stroke, 22(3), 312–318.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Samuel Lockhart
    • 1
  • Charles DeCarli
    • 1
  • Rosemary Fama
    • 2
    • 3
    Email author
  1. 1.Department of Neurology and Center for NeuroscienceUniversity of California, DavisDavisUSA
  2. 2.Department of Psychiatry and Behavioral SciencesStanford University School of MedicineStanfordUSA
  3. 3.Neuroscience Program, SRI InternationalMenlo ParkUSA

Personalised recommendations