Brain Imaging and Behavior

, Volume 11, Issue 3, pp 632–639 | Cite as

Cognitive reserve moderates the relationship between neuropsychological performance and white matter fiber bundle length in healthy older adults

  • Laurie M. Baker
  • David H. Laidlaw
  • Ryan Cabeen
  • Erbil Akbudak
  • Thomas E. Conturo
  • Stephen Correia
  • David F. Tate
  • Jodi M. Heaps-Woodruff
  • Matthew R. Brier
  • Jacob Bolzenius
  • Lauren E. Salminen
  • Elizabeth M. Lane
  • Amanda R. McMichael
  • Robert H. Paul
Original Research

Abstract

Recent work using novel neuroimaging methods has revealed shorter white matter fiber bundle length (FBL) in older compared to younger adults. Shorter FBL also corresponds to poorer performance on cognitive measures sensitive to advanced age. However, it is unclear if individual factors such as cognitive reserve (CR) effectively moderate the relationship between FBL and cognitive performance. This study examined CR as a potential moderator of cognitive performance and brain integrity as defined by FBL. Sixty-three healthy adults underwent neuropsychological evaluation and 3T brain magnetic resonance imaging. Cognitive performance was measured using the Repeatable Battery of Assessment of Neuropsychological Status (RBANS). FBL was quantified from tractography tracings of white matter fiber bundles, derived from the diffusion tensor imaging. CR was determined by estimated premorbid IQ. Analyses revealed that lower scores on the RBANS were associated with shorter whole brain FBL (p = 0.04) and lower CR (p = 0.01) CR moderated the relationship between whole brain FBL and RBANS score (p < 0.01). Tract-specific analyses revealed that CR also moderated the association between FBL in the hippocampal segment of the cingulum and RBANS performance (p = 0.03). These results demonstrate that lower cognitive performance on the RBANS is more common with low CR and short FBL. On the contrary, when individuals have high CR, the relationship between FBL and cognitive performance is attenuated. Overall, CR protects older adults against lower cognitive performance despite age-associated reductions in FBL.

Keywords

Cognition Aging Neuropsychological assessment Diffusion tensor imaging RBANS 

References

  1. Aiken, L. S., West, S. G., & Reno, R. R. (1991). Multiple regression: Testing and interpreting interactions. Sage.Google Scholar
  2. Arenaza-Urquijo, E. M., Bosch, B., Sala-Llonch, R., Solé-Padullés, C., Junqué, C., Fernández-Espejo, D., & Bartrés-Faz, D. (2011). Specific anatomic associations between white matter integrity and cognitive reserve in normal and cognitively impaired elders. The American Journal of Geriatric Psychiatry, 19(1), 33–42.CrossRefPubMedGoogle Scholar
  3. Baek, S. O., Kim, O. L., Kim, S. H., Kim, M. S., Son, S. M., Cho, Y. W., & Jang, S. H. (2013). Relation between cingulum injury and cognition in chronic patients with traumatic brain injury; diffusion tensor tractography study. NeuroRehabilitation, 33(3), 465–471.PubMedGoogle Scholar
  4. Baker, L. M., Laidlaw, D. H., Conturo, T. E., Hogan, J., Zhao, Y., Luo, X., & Paul, R. H. (2014). Impact of advanced age on fiber bundle lengths utilizing diffusion MRI in white matter tracts. Neurology, 83(3), 247–252.CrossRefPubMedPubMedCentralGoogle Scholar
  5. Beaulieu, C. (2002). The basis of anisotropic water diffusion in the nervous system–a technical review. NMR in Biomedicine, 15(7–8), 435–455.CrossRefPubMedGoogle Scholar
  6. Behrman-Lay, A. M., Usher, C., Conturo, T. E., Correia, S., Laidlaw, D. H., Lane, E. M., & Paul, R. H. (2014). Fiber bundle length and cognition: a length-based tractography MRI study. Brain Imaging and Behavior, 1-11.Google Scholar
  7. Bleecker, M. L., Ford, D. P., Celio, M. A., Vaughan, C. G., & Lindgren, K. N. (2007). Impact of cognitive reserve on the relationship of lead exposure and neurobehavioral performance. Neurology, 69(5), 470--476.Google Scholar
  8. Bolzenius, J. D., Laidlaw, D. H., Cabeen, R., Conturo, T. E., McMichael, A. R., Lane, E. M., & Paul, R. H. (2013). Impact of body mass index on fiber bundle lengths among healthy adults. Brain Imaging and Behavior, 7(3), 300–306.CrossRefPubMedGoogle Scholar
  9. Booth, T., Bastin, M. E., Penke, L., Maniega, S. M., Murray, C., Royle, N. A., & Starr, J. M. (2013). Brain white matter tract integrity and cognitive abilities in community-dwelling older people: the Lothian birth cohort, 1936. Neuropsychology, 27(5), 595.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Brickman, A. M., Siedlecki, K. L., Muraskin, J., Manly, J. J., Luchsinger, J. A., Yeung, L. K., & Stern, Y. (2011). White matter hyperintensities and cognition: testing the reserve hypothesis. Neurobiology of Aging, 32(9), 1588–1598.CrossRefPubMedGoogle Scholar
  11. Chambers, J. M., Freeny, A., & Heiberger, R. M. (1992). Chapter 5 of statistical models in S. Analysis of Variance; Designed Experiments. Wadsworth & Brooks/Cole, Pacific Grove.Google Scholar
  12. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Hillsdale: Erlbaum.Google Scholar
  13. Conturo, T. E., McKinstry, R. C., Akbudak, E., & Robinson, B. H. (1996). Encoding of anisotropic diffusion with tetrahedral gradients: a general mathematical diffusion formalism and experimental results. Magnetic Resonance in Medicine, 35(3), 399--412.Google Scholar
  14. Conturo, T. E., Lori, N. F., Cull, T. S., Akbudak, E., Snyder, A. Z., Shimony, J. S., & Raichle, M. E. (1999). Tracking neuronal fiber pathways in the living human brain. Proceedings of the National Academy Of Sciences, 96(18), 10422–10427.CrossRefGoogle Scholar
  15. Correia, S., Lee, S. Y., Voorn, T., Tate, D. F., Paul, R. H., Zhang, S., & Laidlaw, D. H. (2008). Quantitive tractography metrics of white matter integrity in diffusion-tensor MRI. NeuroImage, 42, 568–581.CrossRefPubMedPubMedCentralGoogle Scholar
  16. Cremers, L. G., de Groot, M., Hofman, A., Krestin, G. P., van der Lugt, A., Niessen, W. J., & Ikram, M. A. (2016). Altered tract-specific white matter microstructure is related to poorer cognitive performance. The Rotterdam Study. Neurobiology of Aging, 39, 108–117.CrossRefPubMedGoogle Scholar
  17. Dufouil, C., Alperovitch, A., & Tzourio, C. (2003). Influence of education on the relationship between white matter lesions and cognition. Neurology, 60(5), 831–836.CrossRefPubMedGoogle Scholar
  18. Filippi, M., Cercignani, M., Inglese, M., Horsfield, M. A., & Comi, G. (2001). Diffusion tensor magnetic resonance imaging in multiple sclerosis. Neurology, 56(3), 304–311.CrossRefPubMedGoogle Scholar
  19. Foley, J. M., Ettenhofer, M. L., Kim, M. S., Behdin, N., Castellon, S. A., & Hinkin, C. H. (2012). Cognitive reserve as a protective factor in older HIV-positive patients at risk for cognitive decline. Applied Neuropsychology: Adult, 19(1), 16--25.Google Scholar
  20. Glutting, J., & Wilkinson, G. (2005). Wide range achievement test (WRAT-4). Austin: Pro-Ed.Google Scholar
  21. Hagman, P., Jonasson, L., Maeder, P., Thiran, J. P., Wedeen, V. J., & Meuli, R. (2006). Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiographics, 26(suppl_1), S205–S223.CrossRefGoogle Scholar
  22. Hajnal, J. V., Bryant, D. J., Kasuboski, L., Pattany, P. M., De Coene, B., Lewis, P. D., et al. (1992). Use of fluid attenuated inversion recovery (FLAIR) pulse sequences in MRI of the brain. Journal of Computer Assisted Tomography, 16, 841–844.CrossRefPubMedGoogle Scholar
  23. Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and acute linear registration and motion correction of brain images. NeuroImage, 17(2), 825–841.CrossRefPubMedGoogle Scholar
  24. Jeong, H., Kim, J., Choi, H. S., Kim, E. S., Kim, D. S., Shim, K. W., & Lee, S. K. (2011). Changes in integrity of normal-appearing white matter in patients with moyamoya disease: a diffusion tensor imaging study. American Journal of Neuroradiology, 32(10), 1893–1898.CrossRefPubMedGoogle Scholar
  25. Kesler, S. R., Adams, H. F., Blasey, C. M., & Bigler, E. D. (2003). Premorbid intellectual functioning, education, and brain size in traumatic brain injury: An investigation of the cognitive reserve hypothesis. Applied Neuropsychology, 10(3), 153–162.CrossRefPubMedGoogle Scholar
  26. Lane, E. M., Paul, R. H., Moser, D. J., Fletcher, T. D., & Cohen, R. A. (2011). Influence of education on subcortical hyperintensities and global cognitive status in vascular dementia. Journal of the International Neuropsychological Society, 17(03), 531–536.CrossRefPubMedPubMedCentralGoogle Scholar
  27. Lori, N. F., Akbudak, E., Shimony, J. S., Cull, T. S., Snyder, A. Z., Guillory, R. K., & Conturo, T. E. (2002). Diffusion tensor fiber tracking of human brain connectivity: acquisition methods, reliability analysis and biological results. NMR in Biomedicine, 15, 494–515.CrossRefPubMedGoogle Scholar
  28. Madden, D. J., Bennett, I. J., & Song, A. W. (2009). (2009). cerebral white matter integrity and cognitive aging: contributions from diffusion tensor imaging. Neuropsychology Review, 19, 415–435.CrossRefPubMedPubMedCentralGoogle Scholar
  29. Marner, L., Nyengaard, J. R., Tang, Y., & Pakkenberg, B. (2003). Marked loss of myelinated nerve fibers in the human brain with age. The Journal of Comparative Neurology, 462(2), 144–152.CrossRefPubMedGoogle Scholar
  30. Mori, S., Crain, B. J., Chacko, V. P., & van Zijl, P. C. (1999). Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Annals of Neurology, 45(2), 265–269.CrossRefPubMedGoogle Scholar
  31. Mortimer, J. A., Snowdon, D. A., & Markesbery, W. R. (2003). Head circumference, education and risk of dementia: findings from the nun study. Journal of Clinical and Experimental Neuropsychology, 25(5), 671–679.CrossRefPubMedGoogle Scholar
  32. Mugler, J. P., & Brookeman, J. R. (1990). Three-dimensional magnetization-prepared rapid gradient-echo imaging (3D MP RAGE). Magnetic Resonance in Medicine, 15(1), 152–157.Google Scholar
  33. Murray, A. D., Staff, R. T., McNeil, C. J., Salarirad, S., Ahearn, T. S., Mustafa, N., & Whalley, L. J. (2011). The balance between cognitive reserve and brain imaging biomarkers of cerebrovascular and Alzheimer’s diseases. Brain, 134(Pt 12), 3687–3696.CrossRefPubMedGoogle Scholar
  34. Nebes, R. D., Meltzer, C. C., Whyte, E. M., Scanlon, J. M., Halligan, E. M., Saxton, J. A., ... & DeKosky, S. T. (2006). The relation of white matter hyperintensities to cognitive performance in the normal old: education matters. Aging, Neuropsychology, and Cognition, 13(3-4), 326--340.Google Scholar
  35. Peters, A. (2002). The effects of normal aging on myelin and nerve fibers: a review. Journal of Neurocytology, 31(8–9), 581–593.CrossRefPubMedGoogle Scholar
  36. Randolph, C. (1998). Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). San Antonio: Psychological Corporation.Google Scholar
  37. Salat, D. H., Tuch, D. S., Hevelone, N. D., Fischl, B., Corkin, S., Rosas, H. D., & Dale, A. M. (2005). Age-related changes in prefrontal white matter measured by diffusion tensor imaging. Annals of the New York Academy of Sciences, 1064(1), 37–49.CrossRefPubMedGoogle Scholar
  38. Salminen, L. E., Schofield, P. R., Lane, E. M., Heaps, J. M., Pierce, K. D., Cabeen, R., & Paul, R. H. (2013). Neuronal fiber bundle lengths in healthy adult carriers of the ApoE4 allele: A quantitative tractography DTI study. Brain Imaging and Behavior, 7(3), 274–281.CrossRefPubMedGoogle Scholar
  39. Sánchez, J. L., Rodríguez, M., & Carro, J. (2002). Influence of cognitive reserve on neuropsychologic functioning in Alzheimer’s disease type sporadic in subjects of Spanish nationality. Cognitive and Behavioral Neurology, 15(2), 113–122.Google Scholar
  40. Satz, P. (1993). Brain reserve capacity on symptom onset after brain injury: a formulation and review of evidence for threshold theory. Neuropsychology, 7(3), 273.CrossRefGoogle Scholar
  41. Sawrie, S. M., Martin, R. C., Faught, R. E., Maton, B., Hugg, J. W., & Kuzniecky, R. I. (2000). Nonlinear trends in hippocampal metabolic function and verbal memory: evidence of Cognitive Reserve in temporal lobe epilepsy? Epilepsy & Behavior, 1(2), 106–111.CrossRefGoogle Scholar
  42. Schmidt, R., Schmidt, H., Haybaeck, J., Loitfelder, M., Weis, S., Cavalieri, M., & Jellinger, K. (2011). Heterogeneity in age-related white matter changes. Acta Neuropathologica, 122(2), 171–185.CrossRefPubMedGoogle Scholar
  43. Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143–155.CrossRefPubMedGoogle Scholar
  44. Soares, J. M., Marques, P., Alves, V., & Sousa, N. (2013). A hitchhiker’s guide to diffusion tensor imaging. Frontiers in Neuroscience, 7(31), 1–14.Google Scholar
  45. Stern, Y. (2002). What is cognitive reserve? theory and research application of the reserve concept. Journal of the International Neuropsychological Society, 8(03), 448–460.CrossRefPubMedGoogle Scholar
  46. Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47(10), 2015–2028.CrossRefPubMedPubMedCentralGoogle Scholar
  47. Stern, R. A., Silva, S. G., Chaisson, N., & Evans, D. L. (1996). Influence of cognitive reserve on neuropsychological functioning in asymptomatic human immunodeficiency virus-1 infection. Archives of Neurology, 53(2), 148–153.CrossRefPubMedGoogle Scholar
  48. Sun, S. W., Neil, J. J., Liang, H. F., He, Y. Y., Schmidt, R. E., Hsu, C. Y., & Song, S. K. (2005). Formalin fixation alters water diffusion coefficient magnitude but not anisotropy in infarcted brain. Magnetic Resonance in Medicine, 53(6), 1447–1451.CrossRefPubMedGoogle Scholar
  49. Tang, Y., Nyengaard, J. R., Pakkenberg, B., & Gundersen, H. J. G. (1997). Age-induced white matter changes in the human brain: a stereological investigation. Neurobiology of Aging, 18(6), 609–615.CrossRefPubMedGoogle Scholar
  50. Voineskos, A. N., Rajji, T. K., Lobaugh, N. J., Miranda, D., Shenton, M. E., Kennedy, J. L., & Mulsant, B. H. (2012). Age-related decline in white matter tract integrity and cognitive performance: a DTI tractography and structural equation modeling study. Neurobiology of Aging, 33(1), 21–34.CrossRefPubMedGoogle Scholar
  51. Zhang, S., Demiralp, C., & Laidlaw, D. H. (2003). Visualizing diffusion tensor MR images using streamtubes and streamsurfaces. Visualization and Computer Graphics, IEEE Transactions on, 9(4), 454--462.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Laurie M. Baker
    • 1
  • David H. Laidlaw
    • 2
  • Ryan Cabeen
    • 2
  • Erbil Akbudak
    • 3
  • Thomas E. Conturo
    • 3
  • Stephen Correia
    • 4
  • David F. Tate
    • 5
  • Jodi M. Heaps-Woodruff
    • 5
  • Matthew R. Brier
    • 6
  • Jacob Bolzenius
    • 5
  • Lauren E. Salminen
    • 1
  • Elizabeth M. Lane
    • 7
  • Amanda R. McMichael
    • 3
  • Robert H. Paul
    • 1
    • 5
  1. 1.Department of Psychological SciencesUniversity of Missouri – Saint LouisSaint LouisUSA
  2. 2.Computer Science DepartmentBrown UniversityProvidenceUSA
  3. 3.Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisUSA
  4. 4.Division of Biology and MedicineBrown Medical SchoolProvidenceUSA
  5. 5.Missouri Institute of Mental HealthSt. LouisUSA
  6. 6.Department of NeurologyWashington University School of MedicineSt. LouisUSA
  7. 7.Department of NeurologyVanderbilt University Medical CenterNashvilleUSA

Personalised recommendations