Gray matter volume covariance patterns associated with gait speed in older adults: a multi-cohort MRI study

  • Helena M. Blumen
  • Lucy L. Brown
  • Christian Habeck
  • Gilles Allali
  • Emmeline Ayers
  • Olivier Beauchet
  • Michele Callisaya
  • Richard B. Lipton
  • P. S. Mathuranath
  • Thanh G. Phan
  • V. G. Pradeep Kumar
  • Velandai Srikanth
  • Joe Verghese
Original Research
  • 78 Downloads

Abstract

Accelerated gait decline in aging is associated with many adverse outcomes, including an increased risk for falls, cognitive decline, and dementia. Yet, the brain structures associated with gait speed, and how they relate to specific cognitive domains, are not well-understood. We examined structural brain correlates of gait speed, and how they relate to processing speed, executive function, and episodic memory in three non-demented and community-dwelling older adult cohorts (Overall N = 352), using voxel-based morphometry and multivariate covariance-based statistics. In all three cohorts, we identified gray matter volume covariance patterns associated with gait speed that included brain stem, precuneus, fusiform, motor, supplementary motor, and prefrontal (particularly ventrolateral prefrontal) cortex regions. Greater expression of these gray matter volume covariance patterns linked to gait speed were associated with better processing speed in all three cohorts, and with better executive function in one cohort. These gray matter covariance patterns linked to gait speed were not associated with episodic memory in any of the cohorts. These findings suggest that gait speed, processing speed (and to some extent executive functions) rely on shared neural systems that are subject to age-related and dementia-related change. The implications of these findings are discussed within the context of the development of interventions to compensate for age-related gait and cognitive decline.

Keywords

Gait Cognition Magnetic resonance imaging Gray matter Multivariate analyses 

Notes

Acknowledgements

We would like to thank Melanie Lucas, Syed Sabbir, Susmit Tripathi and Jennifer Yuan for their assistance in manually re-orienting, and ensuring proper segmentation of, neuroimaging data.

Compliance with ethical standards

Conflict of Interest

All authors declare that he/she has no conflict of interest.

Ethical approval

All procedures performed in these studies involving human subjects were in accordance with the ethical standards of the institutions, and with the 1964 Helsinki declaration and its later amendments.

Informed consent

Informed consent was obtained from all individual participants included in each study.

Supplementary material

11682_2018_9871_MOESM1_ESM.docx (19 kb)
ESM 1 (DOCX 19 kb)

References

  1. Abellan van Kan, G., Rolland, Y., Andrieu, S., Bauer, J., Beauchet, O., Bonnefoy, M., Cesari, M., Donini, L. M., Gillette Guyonnet, S., Inzitari, M., Nourhashemi, F., Onder, G., Ritz, P., Salva, A., Visser, M., & Vellas, B. (2009). Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) task force. The Journal of Nutrition, Health & Aging, 13, 881–889.CrossRefGoogle Scholar
  2. Allali, G., van der Meulen, M., Beauchet, O., Rieger, S. W., Vuilleumier, P., & Assal, F. (2014). The neural basis of age-related changes in motor imagery of gait: an fMRI study. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 69, 1389–1398.CrossRefPubMedGoogle Scholar
  3. Allali, G., Annweiler, C., Blumen, H., Callisaya, M., De Cock, A.M., Kressig, R., Srikanth, V., Steinmetz, J.P., Verghese, J., Beauchet, O. (2015). Gait phenotype from mild cognitive impairment to moderate dementia: results from the GOOD initiative. European Journal of Neurology.Google Scholar
  4. Alvarez, J. A., & Emory, E. (2006). Executive function and the frontal lobes: a meta-analytic review. Neuropsychology Review, 16, 17–42.CrossRefPubMedGoogle Scholar
  5. Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. NeuroImage, 38, 95–113.CrossRefPubMedGoogle Scholar
  6. Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26, 839–851.CrossRefPubMedGoogle Scholar
  7. Ashby, F. G. (2011). Statistical analysis of fMRI data. In MIT press.Google Scholar
  8. Association, A. P. (2000). Diagnostic and statistical manual of mental disorders (revised 4th ed.). Washington, DC: American Psychiatric Association.Google Scholar
  9. Atkinson, H. H., Rosano, C., Simonsick, E. M., Williamson, J. D., Davis, C., Ambrosius, W. T., Rapp, S. R., Cesari, M., Newman, A. B., Harris, T. B., Rubin, S. M., Yaffe, K., Satterfield, S., & Kritchevsky, S. B. (2007). Cognitive function, gait speed decline, and comorbidities: the health, aging and body composition study. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 62, 844–850.CrossRefGoogle Scholar
  10. Beauchet, O., Allali, G., Launay, C., Herrmann, F., & Annweiler, C. (2013). Gait variability at fast-pace walking speed: a biomarker of mild cognitive impairment? The Journal of Nutrition, Health & Aging, 17, 235–239.CrossRefGoogle Scholar
  11. Beauchet, O., Allali, G., Annweiler, C., & Verghese, J. (2016a). Association of motoric cognitive risk syndrome with brain volumes: results from the gait study. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 71, 1081–1088.CrossRefPubMedGoogle Scholar
  12. Beauchet, O., Annweiler, C., Callisaya, M. L., De Cock, A. M., Helbostad, J. L., Kressig, R. W., Srikanth, V., Steinmetz, J. P., Blumen, H. M., Verghese, J., & Allali, G. (2016b). Poor gait performance and prediction of dementia: results from a meta-analysis. Journal of the American Medical Directors Association.Google Scholar
  13. Beauchet, O., Blumen, H.M., Callisaya, M.L., De Cock, A.M., Kressig, R.W., Srikanth, V., Steinmetz, J.P., Annweiler, C., Allali, G., 2016c. Times are changing; researchers need to change too. European Journal of Neurology 23, e10.Google Scholar
  14. Blumen, H. M., Holtzer, R., Brown, L. L., Gazes, Y., & Verghese, J. (2014). Behavioral and neural correlates of imagined walking and walking while talking in the elderly. Human Brain Mapping, 35, 4090–4104.CrossRefPubMedPubMedCentralGoogle Scholar
  15. Buracchio, T., Dodge, H. H., Howieson, D., Wasserman, D., & Kaye, J. (2010). The trajectory of gait speed preceding mild cognitive impairment. Archives of Neurology, 67, 980–986.CrossRefPubMedPubMedCentralGoogle Scholar
  16. Burnham, K.P., Anderson, D.R. (2002). Model selection and multimodel inference: a practical information-theoretic approach. Springer Science & Business Media.Google Scholar
  17. Buschke, H. (1973). Selective reminding for analysis of memory and learning. Journal of Verbal Learning and Verbal Behavior, 12, 543–550.CrossRefGoogle Scholar
  18. Callisaya, M. L., Beare, R., Phan, T. G., Blizzard, L., Thrift, A. G., Chen, J., & Srikanth, V. K. (2013). Brain structural change and gait decline: a longitudinal population-based study. Journal of the American Geriatrics Society, 61, 1074–1079.CrossRefPubMedGoogle Scholar
  19. Callisaya, M. L., Beare, R., Phan, T. G., Chen, J., & Srikanth, V. K. (2014). Global and regional associations of smaller cerebral gray and white matter volumes with gait in older people. PLoS One, 9, e84909.CrossRefPubMedPubMedCentralGoogle Scholar
  20. Callisaya, M. L., Blizzard, C. L., Wood, A. G., Thrift, A. G., Wardill, T., & Srikanth, V. K. (2015). Longitudinal relationships between cognitive decline and gait slowing: the tasmanian study of cognition and gait. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 70, 1226–1232.CrossRefGoogle Scholar
  21. Ceccarelli, A., Rocca, M. A., Pagani, E., Falini, A., Comi, G., & Filippi, M. (2009). Cognitive learning is associated with gray matter changes in healthy human individuals: a tensor-based morphometry study. NeuroImage, 48, 585–589.CrossRefPubMedGoogle Scholar
  22. Colcombe, S. J., Erickson, K. I., Scalf, P. E., Kim, J. S., Prakash, R., McAuley, E., Elavsky, S., Marquez, D. X., Hu, L., & Kramer, A. F. (2006). Aerobic exercise training increases brain volume in aging humans. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 61, 1166–1170.CrossRefGoogle Scholar
  23. Cosentino, S., Brickman, A. M., Griffith, E., Habeck, C., Cines, S., Farrell, M., Shaked, D., Huey, E. D., Briner, T., & Stern, Y. (2015). The right insula contributes to memory awareness in cognitively diverse older adults. Neuropsychologia, 75, 163–169.CrossRefPubMedPubMedCentralGoogle Scholar
  24. Cummings, S. R., Studenski, S., & Ferrucci, L. (2014). A diagnosis of dismobility--giving mobility clinical visibility: a mobility working group recommendation. JAMA, 311, 2061–2062.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Doi, T., Shimada, H., Makizako, H., Tsutsumimoto, K., Uemura, K., Anan, Y., & Suzuki, T. (2014). Cognitive function and gait speed under normal and dual-task walking among older adults with mild cognitive impairment. BMC Neurology, 14, 67.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Dumurgier, J., Crivello, F., Mazoyer, B., Ahmed, I., Tavernier, B., Grabli, D., Francois, C., Tzourio-Mazoyer, N., Tzourio, C., & Elbaz, A. (2012). MRI atrophy of the caudate nucleus and slower walking speed in the elderly. NeuroImage, 60, 871–878.CrossRefPubMedGoogle Scholar
  27. Efron, B., Tibshirani, R.J. (1994). An introduction to the bootstrap. CRC press.Google Scholar
  28. Elderkin-Thompson, V., Ballmaier, M., Hellemann, G., Pham, D., & Kumar, A. (2008). Executive function and MRI prefrontal volumes among healthy older adults. Neuropsychology, 22, 626–637.CrossRefPubMedGoogle Scholar
  29. Erickson, K. I., Voss, M. W., Prakash, R. S., Basak, C., Szabo, A., Chaddock, L., Kim, J. S., Heo, S., Alves, H., White, S. M., Wojcicki, T. R., Mailey, E., Vieira, V. J., Martin, S. A., Pence, B. D., Woods, J. A., McAuley, E., & Kramer, A. F. (2011). Exercise training increases size of hippocampus and improves memory. Proceedings of the National Academy of Sciences, 108, 3017–3022.CrossRefGoogle Scholar
  30. Ezzati, A., Katz, M. J., Lipton, M. L., Lipton, R. B., & Verghese, J. (2015). The association of brain structure with gait velocity in older adults: a quantitative volumetric analysis of brain MRI. Neuroradiology, 57, 851–861.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 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, 189–198.CrossRefPubMedGoogle Scholar
  32. Friston, K. J., Holmes, A. P., Poline, J. B., Grasby, P. J., Williams, S. C., Frackowiak, R. S., & Turner, R. (1995). Analysis of fMRI time-series revisited. NeuroImage, 2, 45–53.CrossRefPubMedGoogle Scholar
  33. Garcia-Rill, E. (1991). The pedunculopontine nucleus. Progress in Neurobiology, 36, 363–389.CrossRefPubMedGoogle Scholar
  34. Habeck, C., & Stern, Y. (2007). Neural network approaches and their reproducibility in the study of verbal working memory and Alzheimer's disease. Clinical Neuroscience Research, 6, 381–390.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Habeck, C., & Stern, Y. (2010). Multivariate data analysis for neuroimaging data: overview and application to Alzheimer’s Disease. Cell Biochemistry and Biophysics, 58, 53–67.CrossRefPubMedPubMedCentralGoogle Scholar
  36. Habeck, C., Krakauer, J. W., Ghez, C., Sackeim, H. A., Eidelberg, D., Stern, Y., & Moeller, J. R. (2005a). A new approach to spatial covariance modeling of functional brain imaging data: ordinal trend analysis. Neural Computation, 17, 1602–1645.CrossRefPubMedGoogle Scholar
  37. Habeck, C., Rakitin, B. C., Moeller, J., Scarmeas, N., Zarahn, E., Brown, T., & Stern, Y. (2005b). An event-related fMRI study of the neural networks underlying the encoding, maintenance, and retrieval phase in a delayed-match-to-sample task. Brain Research. Cognitive Brain Research, 23, 207–220.CrossRefPubMedGoogle Scholar
  38. Habeck, C., Foster, N. L., Perneczky, R., Kurz, A., Alexopoulos, P., Koeppe, R. A., Drzezga, A., & Stern, Y. (2008). Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease. NeuroImage, 40, 1503–1515.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Habeck, C., Gazes, Y., Razlighi, Q., Steffener, J., Brickman, A., Barulli, D., Salthouse, T., & Stern, Y. (2016). The reference ability neural network study: life-time stability of reference-ability neural networks derived from task maps of young adults. NeuroImage, 125, 693–704.CrossRefPubMedGoogle Scholar
  40. Haber, S.N. (2016). Corticostriatal circuitry. Neuroscience in the 21st Century, 1–21.Google Scholar
  41. Holtzer, R., Verghese, J., Xue, X., & Lipton, R. B. (2006). Cognitive processes related to gait velocity: results from the Einstein aging study. Neuropsychology, 20, 215–223.CrossRefPubMedGoogle Scholar
  42. Holtzer, R., Wang, C., Lipton, R., & Verghese, J. (2012). The protective effects of executive functions and episodic memory on gait speed decline in aging defined in the context of cognitive reserve. Journal of the American Geriatrics Society, 60, 2093–2098.PubMedPubMedCentralGoogle Scholar
  43. Holtzer, R., Epstein, N., Mahoney, J. R., Izzetoglu, M., & Blumen, H. M. (2014a). Neuroimaging of mobility in aging: a targeted review. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 69, 1375–1388.CrossRefPubMedPubMedCentralGoogle Scholar
  44. Holtzer, R., Mahoney, J., & Verghese, J. (2014b). Intraindividual variability in executive functions but not speed of processing or conflict resolution predicts performance differences in gait speed in older adults. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 69, 980–986.CrossRefPubMedGoogle Scholar
  45. Holtzer, R., Wang, C., & Verghese, J. (2014c). Performance variance on walking while talking tasks: theory, findings, and clinical implications. Age, 36, 373–381.CrossRefPubMedGoogle Scholar
  46. Iseki, K., Hanakawa, T., Shinozaki, J., Nankaku, M., & Fukuyama, H. (2008). Neural mechanisms involved in mental imagery and observation of gait. NeuroImage, 41, 1021–1031.CrossRefPubMedGoogle Scholar
  47. Jahn, K., Deutschlander, A., Stephan, T., Strupp, M., Wiesmann, M., & Brandt, T. (2004). Brain activation patterns during imagined stance and locomotion in functional magnetic resonance imaging. NeuroImage, 22, 1722–1731.CrossRefPubMedGoogle Scholar
  48. Kail, R., & Salthouse, T. A. (1994). Processing speed as a mental capacity. Acta Psychologica, 86, 199–225.CrossRefPubMedGoogle Scholar
  49. Karas, G., Scheltens, P., Rombouts, S., Visser, P., Van Schijndel, R., Fox, N., & Barkhof, F. (2004). Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease. NeuroImage, 23, 708–716.CrossRefPubMedGoogle Scholar
  50. Katz, M. J., Lipton, R. B., Hall, C. B., Zimmerman, M. E., Sanders, A. E., Verghese, J., Dickson, D. W., & Derby, C. A. (2012). Age and sex specific prevalence and incidence of mild cognitive impairment, dementia and Alzheimer’s dementia in blacks and whites: a report from the einstein aging study. Alzheimer Disease and Associated Disorders, 26, 335–343.CrossRefPubMedPubMedCentralGoogle Scholar
  51. Katzman, R., Brown, T., Fuld, P., Peck, A., Schechter, R., & Schimmel, H. (1983). Validation of a short orientation-memory-concentration test of cognitive impairment. American Journal of Psychiatry, 140, 734–739.CrossRefPubMedGoogle Scholar
  52. Klein, T. A., Endrass, T., Kathmann, N., Neumann, J., von Cramon, D. Y., & Ullsperger, M. (2007). Neural correlates of error awareness. NeuroImage, 34, 1774–1781.CrossRefPubMedGoogle Scholar
  53. Koechlin, E., Ody, C., & Kouneiher, F. (2003). The architecture of cognitive control in the human prefrontal cortex. Science, 302, 1181–1185.CrossRefPubMedGoogle Scholar
  54. Kühn, S., Gleich, T., Lorenz, R., Lindenberger, U., & Gallinat, J. (2014). Playing Super Mario induces structural brain plasticity: gray matter changes resulting from training with a commercial video game. Molecular Psychiatry, 19, 265–271.CrossRefPubMedGoogle Scholar
  55. la Fougere, C., Zwergal, A., Rominger, A., Forster, S., Fesl, G., Dieterich, M., Brandt, T., Strupp, M., Bartenstein, P., & Jahn, K. (2010). Real versus imagined locomotion: a [18F]-FDG PET-fMRI comparison. NeuroImage, 50, 1589–1598.CrossRefPubMedGoogle Scholar
  56. Lambert, C., Benjamin, P., Zeestraten, E., Lawrence, A. J., Barrick, T. R., & Markus, H. S. (2016). Longitudinal patterns of leukoaraiosis and brain atrophy in symptomatic small vessel disease. Brain.Google Scholar
  57. Lee, S., Habeck, C., Razlighi, Q., Salthouse, T., & Stern, Y. (2016). Selective association between cortical thickness and reference abilities in normal aging. NeuroImage, 142, 293–300.CrossRefPubMedPubMedCentralGoogle Scholar
  58. Leisman, G., Moustafa, A. A., & Shafir, T. (2016). Thinking, walking, talking: integratory motor and cognitive brain function. Frontiers in Public Health, 4, 94.CrossRefPubMedPubMedCentralGoogle Scholar
  59. Levy, B. J., & Wagner, A. D. (2011). Cognitive control and right ventrolateral prefrontal cortex: reflexive reorienting, motor inhibition, and action updating. Annals of the New York Academy of Sciences, 1224, 40–62.CrossRefPubMedPubMedCentralGoogle Scholar
  60. Marinelli, L., Quartarone, A., Hallett, M., Frazzitta, G., & Ghilardi, M. F. (2017). The many facets of motor learning and their relevance for Parkinson's disease. Clinical Neurophysiology, 128, 1127–1141.CrossRefPubMedGoogle Scholar
  61. Marquis, S., Moore, M. M., Howieson, D. B., Sexton, G., Payami, H., Kaye, J. A., & Camicioli, R. (2002). Independent predictors of cognitive decline in healthy elderly persons. Archives of Neurology, 59, 601–606.CrossRefPubMedGoogle Scholar
  62. Mielke, M. M., Roberts, R. O., Savica, R., Cha, R., Drubach, D. I., Christianson, T., Pankratz, V. S., Geda, Y. E., Machulda, M. M., & Ivnik, R. J. (2013). Assessing the temporal relationship between cognition and gait: slow gait predicts cognitive decline in the Mayo Clinic Study of aging. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 68, 929–937.CrossRefGoogle Scholar
  63. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202.CrossRefPubMedGoogle Scholar
  64. Morris, J. C., Heyman, A., Mohs, R. C., Hughes, J. P., van Belle, G., Fillenbaum, G., Mellits, E. D., & Clark, C. (1989). The consortium to establish a registry for Alzheimer's disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer's disease. Neurology, 39, 1159–1165.CrossRefPubMedGoogle Scholar
  65. Mortimer, J. A., Ding, D., Borenstein, A. R., DeCarli, C., Guo, Q., Wu, Y., Zhao, Q., & Chu, S. (2012). Changes in brain volume and cognition in a randomized trial of exercise and social interaction in a community-based sample of non-demented Chinese elders. Journal of Alzheimer's Disease, 30, 757–766.PubMedPubMedCentralGoogle Scholar
  66. Nadkarni, N. K., Nunley, K. A., Aizenstein, H., Harris, T. B., Yaffe, K., Satterfield, S., Newman, A. B., Rosano, C., & Study, f.t.H.A. (2014). Association between cerebellar gray matter volumes, gait speed, and information-processing ability in older adults enrolled in the health ABC study. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 69, 996–1003.CrossRefGoogle Scholar
  67. Newman, A. B., Simonsick, E. M., Naydeck, B. L., Boudreau, R. M., Kritchevsky, S. B., Nevitt, M. C., Pahor, M., Satterfield, S., Brach, J. S., Studenski, S. A., & Harris, T. B. (2006). Association of long-distance corridor walk performance with mortality, cardiovascular disease, mobility limitation, and disability. Journal of the American Medical Association, 295, 2018–2026.CrossRefPubMedGoogle Scholar
  68. Norman, D. A., & Shallice, T. (1980). Attention to action: willed and automatic control of behavior. San Diego, La Jolla, Calif: Center for Human Information Processing, University of California.Google Scholar
  69. Panel on Prevention of Falls in Older Persons, A.G.S., British Geriatrics, S. (2011). Summary of the updated American geriatrics society/British geriatrics society clinical practice guideline for prevention of falls in older persons. Journal of the American Geriatrics Society, 59, 148–157.CrossRefGoogle Scholar
  70. Petersen, R. C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of Internal Medicine, 256, 183–194.CrossRefPubMedGoogle Scholar
  71. Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999). Mild cognitive impairment: clinical characterization and outcome. Archives of Neurology, 56, 303–308.CrossRefPubMedGoogle Scholar
  72. Petersen, R. C., Roberts, R. O., Knopman, D. S., Boeve, B. F., Geda, Y. E., Ivnik, R. J., Smith, G. E., & Jack Jr., C. R. (2009). Mild cognitive impairment: ten years later. Archives of Neurology, 66, 1447–1455.CrossRefPubMedPubMedCentralGoogle Scholar
  73. Randolph, C., Tierney, M. C., Mohr, E., & Chase, T. N. (1998). The repeatable battery for the assessment of neuropsychological status (RBANS): preliminary clinical validity. Journal of Clinical and Experimental Neuropsychology, 20, 310–319.CrossRefPubMedGoogle Scholar
  74. Raz, N. (2000). Aging of the brain and its impact on cognitive performance: Integration of structural and functional findings. In F. I. M. C. T. A. Salthouse (Ed.), The handbook of aging and cognition (2nd ed., pp. 1–90). Mahwah: Lawrence Erlbaum Associates Publishers.Google Scholar
  75. Reitan, R. (1978). Manual for administration of neuropsychological test batteries for adults and children. Tucson: Reitan Neuropsychology Laboratories.Google Scholar
  76. Rosano, C., Studenski, S. A., Aizenstein, H. J., Boudreau, R. M., Longstreth Jr., W. T., & Newman, A. B. (2012). Slower gait, slower information processing and smaller prefrontal area in older adults. Age and Ageing, 41, 58–64.CrossRefPubMedGoogle Scholar
  77. Rosso, A.L., Studenski, S.A., Chen, W.G., Aizenstein, H.J., Alexander, N.B., Bennett, D.A., Black, S.E., Camicioli, R., Carlson, M.C., Ferrucci, L., Guralnik, J.M., Hausdorff, J.M., Kaye, J., Launer, L.J., Lipsitz, L.A., Verghese, J., Rosano, C. (2013). Aging, the Central Nervous System, and Mobility. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences.Google Scholar
  78. Rypma, B., Berger, J. S., Prabhakaran, V., Bly, B. M., Kimberg, D. Y., Biswal, B. B., & D'Esposito, M. (2006). Neural correlates of cognitive efficiency. NeuroImage, 33, 969–979.CrossRefPubMedGoogle Scholar
  79. Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403.CrossRefPubMedGoogle Scholar
  80. Shallice, T., Fletcher, P., Frith, C. D., Grasby, P., Frackowiak, R. S. J., & Dolan, R. J. (1994). Brain regions associated with acquisition and retrieval of verbal episodic memory. Nature, 368, 633–635.CrossRefPubMedGoogle Scholar
  81. Society, A. G., Society, G., Of, A. A., & On Falls Prevention, O. S. P. (2001). Guideline for the prevention of falls in older persons. Journal of the American Geriatrics Society, 49, 664–672.CrossRefGoogle Scholar
  82. Spaniol, J., Davidson, P. S., Kim, A. S., Han, H., Moscovitch, M., & Grady, C. L. (2009). Event-related fMRI studies of episodic encoding and retrieval: meta-analyses using activation likelihood estimation. Neuropsychologia, 47, 1765–1779.CrossRefPubMedGoogle Scholar
  83. Spetsieris, P. G., & Eidelberg, D. (2011). Scaled subprofile modeling of resting state imaging data in Parkinson's disease: Methodological issues. NeuroImage, 54, 2899–2914.CrossRefPubMedGoogle Scholar
  84. Srikanth, V., Sanders, L., Callisaya, M., Martin, K., & Phan, T. (2010). Brain aging and gait. Aging Health, 6, 123–131.CrossRefGoogle Scholar
  85. Steffener, J., Brickman, A. M., Habeck, C., Salthouse, T. A., & Stern, Y. (2013). Cerebral blood flow and gray matter volume covariance patterns of cognition in aging. Human Brain Mapping, 34, 3267–3279.CrossRefPubMedGoogle Scholar
  86. Stern, Y., Habeck, C., Steffener, J., Barulli, D., Gazes, Y., Razlighi, Q., Shaked, D., & Salthouse, T. (2014). The reference ability neural network study: motivation, design, and initial feasibility analyses. NeuroImage, 103, 139–151.CrossRefPubMedPubMedCentralGoogle Scholar
  87. Takeuchi, H., Taki, Y., Sassa, Y., Hashizume, H., Sekiguchi, A., Fukushima, A., & Kawashima, R. (2011). Working memory training using mental calculation impacts regional gray matter of the frontal and parietal regions. PLoS One, 6, e23175.CrossRefPubMedPubMedCentralGoogle Scholar
  88. Tattersall, T. L., Stratton, P. G., Coyne, T. J., Cook, R., Silberstein, P., Silburn, P. A., Windels, F., & Sah, P. (2014). Imagined gait modulates neuronal network dynamics in the human pedunculopontine nucleus. Nature Neuroscience, 17, 449–454.CrossRefPubMedGoogle Scholar
  89. Thompson, P.M., Hayashi, K.M., de Zubicaray, G., Janke, A.L., Rose, S.E., Semple, J., Herman, D., Hong, M.S., Dittmer, S.S., Doddrell, D.M., Toga, A.W., 2003. Dynamics of Gray Matter Loss in Alzheimer's Disease. The Journal of Neuroscience 23, 994–1005.Google Scholar
  90. Tulving, E. (1972). Episodic and semantic memory 1. Organization of Memory (Vol. 381). London: Academic.Google Scholar
  91. Tulving, E. (1985). Elements of episodic memory.Google Scholar
  92. Turner, G., & Clegg, A. (2014). Best practice guidelines for the management of frailty: a British geriatrics society, age UK and Royal College of general practitioners report. Age and Ageing, 43, 744–747.CrossRefPubMedGoogle Scholar
  93. van der Meulen, M., Allali, G., Rieger, S. W., Assal, F., Vuilleumier, P. (2012). The influence of individual motor imagery ability on cerebral recruitment during gait imagery. Human Brain Mapping, 35(2), 455–470.Google Scholar
  94. Verghese, J., Lipton, R. B., Hall, C. B., Kuslansky, G., Katz, M. J., & Buschke, H. (2002). Abnormality of gait as a predictor of non-Alzheimer's dementia. New England Journal of Medicine, 347, 1761–1768.CrossRefPubMedGoogle Scholar
  95. Verghese, J., LeValley, A., Hall, C. B., Katz, M. J., Ambrose, A. F., & Lipton, R. B. (2006). Epidemiology of gait disorders in community-residing older adults. Journal of the American Geriatrics Society, 54, 255–261.CrossRefPubMedPubMedCentralGoogle Scholar
  96. Verghese, J., Wang, C., Lipton, R. B., Holtzer, R., & Xue, X. (2007). Quantitative gait dysfunction and risk of cognitive decline and dementia. Journal of Neurology, Neurosurgery, and Psychiatry, 78, 929–935.CrossRefPubMedPubMedCentralGoogle Scholar
  97. Verghese, J., Robbins, M., Holtzer, R., Zimmerman, M., Wang, C., Xue, X., & Lipton, R. B. (2008). Gait dysfunction in mild cognitive impairment syndromes. Journal of the American Geriatrics Society, 56, 1244–1251.CrossRefPubMedPubMedCentralGoogle Scholar
  98. Verghese, J., Holtzer, R., Lipton, R. B., & Wang, C. (2009). Quantitative gait markers and incident fall risk in older adults. Journals of Gerontology Series A: Biological Sciences & Medical Sciences, 64A, 896–901.CrossRefGoogle Scholar
  99. Verghese, J., Mahoney, J., Ambrose, A. F., Wang, C., & Holtzer, R. (2010). Effect of cognitive remediation on gait in sedentary seniors. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 65, 1338–1343.CrossRefGoogle Scholar
  100. Verghese, J., Annweiler, C., Ayers, E., Barzilai, N., Beauchet, O., Bennett, D. A., Bridenbaugh, S. A., Buchman, A. S., Callisaya, M. L., & Camicioli, R. (2014a). Motoric cognitive risk syndrome multicountry prevalence and dementia risk. Neurology, 83, 718–726.CrossRefPubMedPubMedCentralGoogle Scholar
  101. Verghese, J., Ayers, E., Barzilai, N., Bennett, D. A., Buchman, A. S., Aron, S., Holtzer, R., Katz, M., Lipton, R. B., & Wang, C. (2014b). Motoric cognitive risk syndrome: Multicenter incidence study. Neurology, 83, 2278–2284.CrossRefPubMedPubMedCentralGoogle Scholar
  102. Wai, Y.-Y., Wang, J.-J., Weng, Y.-H., Lin, W.-Y., Ma, H.-K., Ng, S.-H., Wan, Y.-L., & Wang, C.-H. (2012). Cortical involvement in a gait-related imagery task: comparison between Parkinson’s disease and normal aging. Parkinsonism & Related Disorders, 18, 537–542.CrossRefGoogle Scholar
  103. Waite, L. M., Grayson, D. A., Piguet, O., Creasey, H., Bennett, H. P., & Broe, G. A. (2005). Gait slowing as a predictor of incident dementia: 6-year longitudinal data from the sydney older persons study. Journal of the Neurological Sciences, 230, 89–93.CrossRefGoogle Scholar
  104. Wang, L., Larson, E. B., Bowen, J. D., & van Belle, G. (2006). Performance-based physical function and future dementia in older people. Archives of Internal Medicine, 166, 1115–1120.CrossRefPubMedGoogle Scholar
  105. Watson, N. L., Rosano, C., Boudreau, R. M., Simonsick, E. M., Ferrucci, L., Sutton-Tyrrell, K., Hardy, S. E., Atkinson, H. H., Yaffe, K., Satterfield, S., Harris, T. B., & Newman, A. B. (2010). Executive function, memory, and gait speed decline in well-functioning older adults. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 65, 1093–1100.CrossRefGoogle Scholar
  106. Zwergal, A., Linn, J., Xiong, G., Brandt, T., Strupp, M., & Jahn, K. (2012). Aging of human supraspinal locomotor and postural control in fMRI. Neurobiology of Aging, 33, 1073–1084.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Helena M. Blumen
    • 1
    • 2
  • Lucy L. Brown
    • 2
  • Christian Habeck
    • 3
  • Gilles Allali
    • 4
  • Emmeline Ayers
    • 1
  • Olivier Beauchet
    • 5
  • Michele Callisaya
    • 6
    • 7
  • Richard B. Lipton
    • 2
  • P. S. Mathuranath
    • 8
  • Thanh G. Phan
    • 6
  • V. G. Pradeep Kumar
    • 9
  • Velandai Srikanth
    • 6
    • 7
  • Joe Verghese
    • 1
    • 2
  1. 1.Department of MedicineAlbert Einstein College of MedicineBronxUSA
  2. 2.Department of NeurologyAlbert Einstein College of MedicineBronxUSA
  3. 3.Cognitive Neuroscience Division, Department of Neurology and Taub Institute for Research on Alzheimer’s disease and the Aging BrainColumbia UniversityNew YorkUSA
  4. 4.Department of Clinical NeurosciencesGeneva University Hospitals and University of GenevaGenevaSwitzerland
  5. 5.Joseph Kaufmann Chair in Geriatric Medicine, Faculty of MedicineMcGill UniversityMontrealCanada
  6. 6.Stroke and Ageing Research Group, Department of Medicine, School of Clinical SciencesMonash UniversityMelbourneAustralia
  7. 7.Menzies Institute for Medical ResearchUniversity of Tasmania (M.L.C.)HobartAustralia
  8. 8.Department of NeurologyNational Institute of Mental Health & NeurosciencesBengaluruIndia
  9. 9.Department of NeurologyBaby Memorial HospitalKozhikodeIndia

Personalised recommendations