Fiber bundle length and cognition: a length-based tractography MRI study

Abstract

Executive function (EF) and cognitive processing speed (CPS) are two cognitive performance domains that decline with advanced age. Reduced EF and CPS are known to correlate with age-related frontal-lobe volume loss. However, it remains unclear whether white matter microstructure in these regions is associated with age-related decline in EF and/or CPS. We utilized quantitative tractography metrics derived from diffusion-tensor MRI to investigate the relationship between the mean fiber bundle lengths (FBLs) projecting to different lobes, and EF/CPS performance in 73 healthy aging adults. We measured aspects of EF and CPS with the Trail Making Test (TMT), Color-Word Interference Test, Letter-Number Sequencing (L-N Seq), and Symbol Coding. Results revealed that parietal and occipital FBLs explained a significant portion of variance in EF. Frontal, temporal, and occipital FBLs explained a significant portion of variance in CPS. Shorter occipital FBLs were associated with poorer performance on the EF tests TMT-B and CWIT 3. Shorter frontal, parietal, and occipital FBLs were associated with poorer performance on L-N Seq and Symbol Coding. Shorter frontal and temporal FBLs were associated with lower performance on CPS tests TMT-A and CWIT 1. Shorter FBLs were also associated with increased age. Results suggest an age-related FBL shortening in specific brain regions related to poorer EF and CPS performance among older adults. Overall, results support both the frontal aging hypothesis and processing speed theory, suggesting that each mechanism is contributing to age-related cognitive decline.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

  1. Albinet, C. T., Boucard, G., Bouquet, C., & Audiffren, M. (2012). Processing speed and executive functions in cognitive aging: How to disentangle their mutual relationship? Brain and Cognition, 79(1), 1–11.

    Article  PubMed  Google Scholar 

  2. Allen, J. S., Bruss, J., Brown, C. K., & Damasio, H. (2005). Normal neuroanatomical variation due to age: The major lobes and a parcellation of the temporal region. Neurobiology of Aging, 26(9), 1245–1260.

    Article  PubMed  Google Scholar 

  3. Baker, L. M., Laidlaw, D. H., Conturo, T. E., Hogan, J., Zhao, Y., Luo, X., et al. (2014). White matter changes with age utilizing quantitative diffusion MRI. Neurology, 83(3), 247–252.

    PubMed Central  Article  PubMed  Google Scholar 

  4. Bartzokis, G. (2004). Age-related myelin breaksown: A developmental model of cognitive decline and Alzheimer’s disease. Neurobiology of Aging, 25, 5–18.

    CAS  Article  PubMed  Google Scholar 

  5. Bartzokis, G., Beckson, M., Neuechterlein, K. H., Edwards, N., & Mintz, J. (2001). Age-related changes in frontal and temporal lobe volumes in men: A magnetic resonance imaging study. Archives of General Psychiatry, 58(5), 461–465.

    CAS  Article  PubMed  Google Scholar 

  6. Bartzokis, G., Cummings, J. L., Sultzer, D., Henderson, V. W., Nuechterlein, K. H., & Mintz, J. (2003). White matter structural integrity in healthy aging adults and patients with Alzheimer’s disease: A magnetic resonance imaging study. Archives of Neurology, 60(3), 393–398.

    Article  PubMed  Google Scholar 

  7. Bartzokis, G., Lu, P. H., Geschwind, D. H., Tingus, K., Huang, D., Mendez, M. F., et al. (2007). Apolipoprotein E affects both myelin breakdown and cognition: Implications for age-related trajectories of decline into dementia. Biological Psychiatry, 62(12), 1380–1387.

    CAS  Article  PubMed  Google Scholar 

  8. Behrens, T. E., Johansen-Berg, H., Woolrich, M. W., Smith, S. M., Wheeler-Kingshott, C. A., Boulby, P. A., et al. (2003). Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nature Neuroscience, 6, 750–757.

    CAS  Article  PubMed  Google Scholar 

  9. Bennett, I. J., Madden, D. J., Vaidya, C. J., Howard, D. V., & Howard, J. H., Jr. (2010). Age-related differences in multiple measures of white matter integrity: A diffusion tensor imaging study of healthy aging. Human Brain Mapping, 31(3), 378–390.

    PubMed Central  PubMed  Google Scholar 

  10. Bolzenius, J. D., Laidlaw, D. H., Cabeen, R. P., Conturo, T. E., McMichael, A. R., Lane, E. M., et al. (2013). Impact of body mass index on neuronal fiber bundle lengths among healthy older adults. Brain Imaging and Behavior, 7(3), 300–306.

    Article  PubMed  Google Scholar 

  11. Brickman, A. M., Zimmerman, M. E., Paul, R. H., Grieve, S. M., Tate, D. F., Cohen, R. A., et al. (2006). Regional white matter and neuropsychological functioning across the adult lifespan. Biological Psychiatry, 60(5), 444–453.

    Article  PubMed  Google Scholar 

  12. Bugg, J. M., Zook, N. A., DeLosh, E. L., Davalos, D. B., & Davis, H. P. (2006). Age differences in fluid intelligence: Contributions of general slowing and frontal decline. Brain and Cognition, 62(1), 9–16.

    Article  PubMed  Google Scholar 

  13. Charlton, R. A., Barrick, T. R., McIntyre, D. J., Shen, Y., O’Sullivan, M., Howe, F. A., et al. (2006). White matter damage on diffusion tensor imaging correlates with age-related cognitive decline. Neurology, 66(2), 217–222.

    CAS  Article  PubMed  Google Scholar 

  14. Charlton, R. A., Landau, S., Schiavone, F., Barrick, T. R., Clark, C. A., Markus, H. S., et al. (2008). A structural equation modeling investigation of age-related variance in executive function and DTI measured white matter damage. Neurobiology of Aging, 29(10), 1547–1555.

    CAS  Article  PubMed  Google Scholar 

  15. 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, 399–412.

    CAS  Article  PubMed  Google Scholar 

  16. Conturo, T. E., Lori, N. F., Cull, T. S., Akbudak, E., Snyder, A. Z., Shimony, J. S., et al. (1999). Tracking neuronal fiber pathways in the living human brain. Proceedings of the National Academy of Sciences of the United States of American, 96(18), 10422–10427.

    CAS  Article  Google Scholar 

  17. Correia, S., Lee, S. Y., Voorn, T., Tate, D. F., Paul, R. H., Zhang, S., et al. (2008). Quantitative tractography metrics of white matter integrity in diffusion-tensor MRI. NeuroImage, 42(2), 568–581.

    PubMed Central  Article  PubMed  Google Scholar 

  18. Cowell, P. E., Turetsky, B. I., Gur, R. C., Grossman, R. I., Shtasel, D. L., & Gur, R. E. (1994). Sex differences in aging of the human frontal and temporal lobes. The Journal of Neuroscience, 14(8), 4748–4755.

    CAS  PubMed  Google Scholar 

  19. Dempster, F. N. (1992). The rise and fall of the inhibitory mechanism: Toward a unified theory of cognitive-development and aging. Developmental Review, 12, 45–75.

    Article  Google Scholar 

  20. Duering, M., Zieren, N., Hervé, D., Jouvent, E., Reyes, S., Peters, N., et al. (2011). Strategic role of frontal white matter tracts in vascular cognitive impairment: A voxel-based lesion-symptom mapping study in CADASIL. Brain, 134(Pt 8), 2366–2375.

    Article  PubMed  Google Scholar 

  21. Fields, R. D. (2008). White matter in learning, cognition and psychiatric disorders. Trends in Neuroscience, 31(7), 361–370.

    CAS  Article  Google Scholar 

  22. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”. a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198.

    CAS  Article  PubMed  Google Scholar 

  23. Fuster, J. M., Baurer, R. H., & Jervey, J. P. (1985). Functional interactions between the inferotemporal and prefrontal cortex in a cognitive task. Brain Research, 330(2), 299–307.

    CAS  Article  PubMed  Google Scholar 

  24. Greenwood, P. M. (2000). The frontal aging hypothesis evaluated. Journal of the International Neuropsychological Society, 6(6), 705–726.

    CAS  Article  PubMed  Google Scholar 

  25. Guttman, C. R., Jolesz, F. A., Kikinis, R., Killiany, R. J., Moss, M. B., Sandor, T., et al. (1998). White matter changes with normal aging. Neurology, 50(4), 972–978.

    Article  Google Scholar 

  26. Jacobs, H. I., Leritz, E. C., Williams, V. J., Van Boxel, M. P., van der Elst, W., Jolles, J., et al. (2013). Association between white matter microstructure, executive functions, and processing speed in older adults: The impact of vascular health. Human Brain Mapping, 34(1), 77–95.

    Article  PubMed  Google Scholar 

  27. Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825–841.

    Article  PubMed  Google Scholar 

  28. Jernigan, T. L., Archibald, S. L., Fennema-Notestine, C., Gamst, A. C., Stout, J. C., Bonner, J., et al. (2001). Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiology of Aging, 22(4), 581–594.

    CAS  Article  PubMed  Google Scholar 

  29. Lee, T., Mosing, M. A., Henry, J. D., Trollor, J. N., Lammel, A., Ames, D., et al. (2012). Genetic influences on five measures of processing speed and their covariation with general cognitive ability in the elderly: The older Australian twins study. Behavior Genetics, 42(1), 96–106.

    Article  PubMed  Google Scholar 

  30. Lori, N. F., Akbudak, E., Shimony, J. S., Cull, T. S., Synder, A. Z., Guillory, R. K., et al. (2002). Diffusion tensor fiber tracking of human brain connectivity: Acquisition methods, reliability analysis and biological results. NMR in Biomedicine, 15(7–8), 494–515.

    CAS  Article  PubMed  Google Scholar 

  31. Lu, P. H., Lee, G. J., Tishler, T. A., Meghpara, M., Thompson, P. M., & Bartzokis, G. (2013). Myelin breakdown mediates age-related slowing in cognitive processing speed in healthy older men. Brain and Cognition, 81(1), 131–138.

    Article  PubMed  Google Scholar 

  32. Madden, D. J., Bennett, I. J., & Song, A. W. (2009). Cerebral white matter integrity and cognitive aging: Contributions from diffusion tensor imaging. Neuropsychology Review, 19(4), 415–435.

    PubMed Central  Article  PubMed  Google Scholar 

  33. Madden, D. J., Spaniol, J., Costello, M. C., Bucur, B., White, L. E., Cabeza, R., et al. (2009). Cerebral white matter integrity mediates adult age differences in cognitive performance. Journal of Cognitive Neuroscience, 21(2), 289–302.

    PubMed Central  Article  PubMed  Google Scholar 

  34. Marner, L., Nyengaard, J. R., Tang, Y., & Pakkenberg, B. (2003). Marked loss of myelinated nerve fibers in the human brain with age. Journal of Comparative Neurology, 462(2), 144–152.

    Article  PubMed  Google Scholar 

  35. Mazziotta, J., Toga, A., Evans, A., Fox, P., Lancaster, J., Zilles, K., et al. (2001). A probabilistic atlas and reference system for the human brain: International Consortium for brain mapping (ICBM). Philosophical Transactions of The Royal Society Biological Sciences, 356(1412), 1293–1322.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  36. McDowell, I., Xi, G., Lindsay, J., & Tukko, H. (2004). Canadian study of health and aging: Study description and patterns of early cognitive decline. Aging, Neuropsychology, and Cognition, 11, 149–168.

    Article  Google Scholar 

  37. Meier-Ruge, W., Ulrich, J., Brühlmann, M., & Meier, E. (1992). Age-related white matter atrophy in the human brain. Annals of the New York Academy of Sciences, 673, 260–269.

    CAS  Article  PubMed  Google Scholar 

  38. Mori, S., Crain, B. J., Chacko, V. P., & Van Zijl, P. (1999). Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Annals of Neurology, 45, 265–269.

    CAS  Article  PubMed  Google Scholar 

  39. Moscovitch, M., & Winocur, G. (1992). The neuropsychology of memory and aging. In F. I. M. Craik & T. A. Salthouse (Eds.), The Handbook of Aging and Cognition (pp. 315–372). New Jersey: Erlbaum.

    Google Scholar 

  40. Mosely, M. (2002). Diffusion tensor imaging and aging – a review. NMR in Biomedicine, 15(7–8), 535–560.

    Google Scholar 

  41. Nucifora, P. G., Verma, R., Lee, S. K., & Melhem, E. R. (2007). Diffusion-Tensor MR imaging and tractography: Exploring brain microstructure and connectivity. Radiology, 245(2), 367–384.

    Article  PubMed  Google Scholar 

  42. O’Sullivan, M., Jones, D. K., Summers, P. E., Morris, R. G., Williams, S. C., & Markus, H. S. (2001). Evidence for cortical “disconnection” as a mechanism of age-related cognitive decline. Neurology, 57(4), 632–638.

    Article  PubMed  Google Scholar 

  43. Paul, R., Lane, E. M., Tate, D. F., Heaps, J., Romo, D. M., Akbudak, E., et al. (2011). Neuroimaging signatures and cognitive correlates of the montreal cognitive assessment screen in a nonclinical elderly sample. Archives of Clinical Neuropsychology, 26(5), 454–460.

    PubMed Central  Article  PubMed  Google Scholar 

  44. Perry, M. E., McDonald, C. R., Hagler, D. J., Jr., Gharapetian, L., Kuperman, J. M., Koyama, A. K., et al. (2009). White matter tracts assocated with set-shifting in healthy aging. Neuropsychologia, 47(13), 2835–2842.

    PubMed Central  Article  PubMed  Google Scholar 

  45. Peters, B. D., Ikuta, T., DeRosse, P., John, M., Burdick, K. E., Gruner, P., et al. (2014). Age-related differences in white matter tract mincrostructure are associated with cognitive performnance from childhood to adulthood. Biological Psychiatry, 75(3), 248–256.

    PubMed Central  Article  PubMed  Google Scholar 

  46. Raz, N., & Rodrigue, K. M. (2006). Differential aging of the brain: Patterns, cognitive correlates and modifiers. Neuroscience & Biobehavioral Reviews, 30(6), 730–748.

    Article  Google Scholar 

  47. 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.

    CAS  Article  PubMed  Google Scholar 

  48. Salat, D. H., Tuch, D. S., Grevea, D. N., Van der Kouwe, A. J., Hevelone, N. D., Zaleta, A. K., et al. (2005). Age-related alterations in white matter microstructure measured by diffusion tensor imaging. Neurobiology of Aging, 26, 1215–1227.

    CAS  Article  PubMed  Google Scholar 

  49. Salminen, L. E., Schofield, P. R., Lane, E. M., Heaps, J. M., Pierce, K. D., Cabeen, R., et al. (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.

    Article  PubMed  Google Scholar 

  50. Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103(3), 403–428.

    CAS  Article  PubMed  Google Scholar 

  51. Schretlen, D., Pearlson, G. D., Anthony, J. C., Aylward, E. H., Augustine, A. M., Davis, A., et al. (2000). Elucidating the contributions of processing speed, executive ability, and frontal lobe volume to normal age-related differences in fluid intelligence. Journal of the International Neuropsychological Society, 6(1), 52–61.

    CAS  Article  PubMed  Google Scholar 

  52. Sowell, E. R., Peterson, B. S., Thompson, P. M., Welcome, S. E., Henkenius, A. L., & Toga, A. W. (2003). Mapping cortical change across the human life span. Nature Neuroscience, 6(3), 309–315.

    CAS  Article  PubMed  Google Scholar 

  53. Sullivan, E. V., & Pfefferbaum, A. (2006). Diffusion tensor imaging and aging. Neuroscience & Biobehavioral Reviews, 30(6), 749–761.

    Article  Google Scholar 

  54. Sun, X., Liang, Y., Wang, J., Chen, K., Chen, Y., Zhou, X., et al. (2014). Early frontal structural and functional changes in mild white matter lesions relevant to cognitive decline. Journal of Alzheimers Disease, 40(1), 123–134.

    Google Scholar 

  55. Tang, Y., Nyengaard, J. R., Pakkenberg, B., & Gundersen, H. J. (1997). Age-induced white matter changes in the human brain: A stereological investigation. Neurobiology of Aging, 18(6), 609–615.

    CAS  Article  PubMed  Google Scholar 

  56. Tate, D. F., Conley, J., Paul, R. H., Coop, K., Zhang, S., Zhou, W., et al. (2010). Quantitative diffusion tensor imaging tractography metrics are associated with cognitive performance among HIV-infected patients. Brain Imaging and Behavior, 4(1), 60–79.

    Article  Google Scholar 

  57. Tekin, S., & Cummings, J. L. (2002). Frontal-subcortical neuronal circuits and clinical neuropsychiatry: An update. Journal of Psychosomatic Research, 53, 647–654.

    Article  PubMed  Google Scholar 

  58. Voineskos, A. N., Rajji, T. K., Lobaugh, N. J., Miranda, D., Senton, M. E., Kennedy, J. L., et al. (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.

    PubMed Central  Article  PubMed  Google Scholar 

  59. Wang, R., Benner, T., Sorensen, A. G., & Wedeen, V. J. (2007, May). Diffusion toolkit: A software package for diffusion imaging data processing and tractography (Abstract #3720). Poster presented at the Joint Annual Meeting of the International Society for Magnetic Resonance Medicine. http://trackvis.org/

  60. West, R. L. (1996). An application of prefrontal cortex function theory to cognitive aging. Psychological Bulletin, 120(2), 272–292.

    CAS  Article  PubMed  Google Scholar 

  61. Yajeya, J., Quintana, J., & Fuster, J. M. (1988). Prefrontal representation of stimulus attributes during delay tasks: II. The role of behavioral significance. Brain Research, 474(2), 222–230.

    CAS  Article  PubMed  Google Scholar 

  62. Zakzanis, K. K., Mraz, R., & Graham, S. J. (2005). An fMRI study of the trail making test. Neuropsychologia, 43(13), 1878–1886.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This study was supported by the following grants: NIH/NINDS grant numbers R01 NS052470 and R01 NS039538, NIH/NIMH grant number R21 MH090494. Recruitment database searches were supported in part by NIH/NCRR grant UL1 TR000448.

Conflict of interest

Ashley M. Behrman-Lay, Christina Usher, Thomas E. Conturo, Stephan Correia, David H. Laidlaw, Elizabeth M. Lane, Jacob Bolzenius, Jodi M. Heaps, Lauren E. Salminen, Laurie M. Baker, Ryan Cabeen, Erbil Akbudak, Xi Luo, Peisi Yan, and Robert H. Paul declare that they have no actual or potential conflicts of interest on this manuscript.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ashley M. Behrman-Lay.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 109 kb)

ESM 2

(DOCX 33 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Behrman-Lay, A.M., Usher, C., Conturo, T.E. et al. Fiber bundle length and cognition: a length-based tractography MRI study. Brain Imaging and Behavior 9, 765–775 (2015). https://doi.org/10.1007/s11682-014-9334-8

Download citation

Keywords

  • Fiber bundle lengths
  • DTI
  • White matter
  • Cognitive processing speed
  • Executive function
  • Aging