Is the Alzheimer’s disease cortical thickness signature a biological marker for memory?

Springer Science and Business Media LLC - Tập 10 - Trang 517-523 - 2015
Edgar Busovaca1, Molly E. Zimmerman2,3, Irene B. Meier1, Erica Y. Griffith1, Stuart M. Grieve4,5,6, Mayuresh S. Korgaonkar4,5,6, Leanne M. Williams7,8, Adam M. Brickman1
1Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, USA
2Department of Psychology, Fordham University, Bronx, USA
3Department of Neurology, Albert Einstein College of Medicine, Bronx, USA.
4Sydney Translational Imaging Laboratory, Sydney Medical School, University of Sydney, Sydney, Australia
5Brain Dynamics Centre, Westmead Millennium Institute, Westmead, Australia
6Sydney Medical School, Westmead, Australia
7Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, USA
8Sierra-Pacific Mental Illness Research, Education, Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, USA

Tóm tắt

Recent work suggests that analysis of the cortical thickness in key brain regions can be used to identify individuals at greatest risk for development of Alzheimer’s disease (AD). It is unclear to what extent this “signature” is a biological marker of normal memory function – the primary cognitive domain affected by AD. We examined the relationship between the AD signature biomarker and memory functioning in a group of neurologically healthy young and older adults. Cortical thickness measurements and neuropsychological evaluations were obtained in 110 adults (age range 21–78, mean = 46) drawn from the Brain Resource International Database. The cohort was divided into young adult (n = 64, age 21–50) and older adult (n = 46, age 51–78) groups. Cortical thickness analysis was performed with FreeSurfer, and the average cortical thickness extracted from the eight regions that comprise the AD signature. Mean AD-signature cortical thickness was positively associated with performance on the delayed free recall trial of a list learning task and this relationship did not differ between younger and older adults. Mean AD-signature cortical thickness was not associated with performance on a test of psychomotor speed, as a control task, in either group. The results suggest that the AD signature cortical thickness is a marker for memory functioning across the adult lifespan.

Tài liệu tham khảo

Arnaiz, E., & Almkvist, O. (2003). Neuropsychological features of mild cognitive impairment and preclinical Alzheimer’s disease. Acta Neurologica Scandinavica. Supplementum, 179, 34–41. Bakkour, A., Morris, J. C., & Dickerson, B. C. (2009). The cortical signature of prodromal AD: regional thinning predicts mild AD dementia. Neurology, 72(12), 1048–1055. Bakkour, A., Morris, J. C., Wolk, D. A., & Dickerson, B. C. (2013). The effects of aging and Alzheimer’s disease on cerebral cortical anatomy: specificity and differential relationships with cognition. NeuroImage, 76, 332–344. doi:10.1016/j.neuroimage.2013.02.059. Braak, H., & Braak, E. (1991). Neuropathological stageing of Alzheimer-related changes. Acta Neuropathologica, 82(4), 239–259. Brickman, A. M., Meier, I. B., Korgaonkar, M. S., Provenzano, F. A., Grieve, S. M., Siedlecki, K. L., & Zimmerman, M. E. (2012). Testing the white matter retrogenesis hypothesis of cognitive aging. Neurobiology of Aging, 33(8), 1699–1715. doi:10.1016/j.neurobiolaging.2011.06.001. Brun, A., & Gustafson, L. (1976). Distribution of cerebral degeneration in Alzheimer’s disease. A clinico-pathological study. Archiv für Psychiatrie und Nervenkrankheiten, 223(1), 15–33. Burns, A., Byrne, E. J., & Maurer, K. (2002). Alzheimer’s disease. Lancet, 360(9327), 163–165. doi:10.1016/S0140-6736(02)09420-5. Clark, C. R., Paul, R. H., Williams, L. M., Arns, M., Fallahpour, K., Handmer, C., & Gordon, E. (2006). Standardized assessment of cognitive functioning during development and aging using an automated touchscreen battery. Archives of Clinical Neuropsychology, 21(5), 449–467. Cosentino, S. A., Brickman, A. M., & Manly, J. J. (2011). Neuropsychological assessment of the dementias of late life. In K. W. Schaie & S. L. Willis (Eds.), Handbook of the psychology of aging (7th ed., pp. 339–352). London: Academic. Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage, 9(2), 179–194. Desikan, R. S., Segonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980. doi:10.1016/j.neuroimage.2006.01.021. Dickerson, B. C., Goncharova, I., Sullivan, M. P., Forchetti, C., Wilson, R. S., Bennett, D. A., & deToledo-Morrell, L. (2001). MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer’s disease. Neurobiology of Aging, 22(5), 747–754. Dickerson, B. C., Bakkour, A., Salat, D. H., Feczko, E., Pacheco, J., Greve, D. N., & Buckner, R. L. (2009). The cortical signature of Alzheimer’s disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cerebral Cortex, 19(3), 497–510. doi:10.1093/cercor/bhn113. Dickerson, B. C., Stoub, T. R., Shah, R. C., Sperling, R. A., Killiany, R. J., Albert, M. S., & Detoledo-Morrell, L. (2011). Alzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adults. Neurology, 76(16), 1395–1402. doi:10.1212/WNL.0b013e3182166e96. Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences of the United States of America, 97(20), 11050–11055. Fischl, B., Sereno, M. I., & Dale, A. M. (1999). Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. NeuroImage, 9(2), 195–207. Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., & Dale, A. M. (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341–355. Flicker, C., Ferris, S. H., & Reisberg, B. (1991). Mild cognitive impairment in the elderly: predictors of dementia. Neurology, 41(7), 1006–1009. Forstl, H., & Kurz, A. (1999). Clinical features of Alzheimer’s disease. European Archives of Psychiatry and Clinical Neuroscience, 249(6), 288–290. Gordon, E., Cooper, N., Rennie, C., Hermens, D., & Williams, L. M. (2005). Integrative neuroscience: the role of a standardized database. Clinical EEG and Neuroscience, 36(2), 64–75. Grieve, S. M., Clark, C. R., Williams, L. M., Peduto, A. J., & Gordon, E. (2005). Preservation of limbic and paralimbic structures in aging. Human Brain Mapping, 25(4), 391–401. doi:10.1002/hbm.20115. Grieve, S. M., Korgaonkar, M. S., Clark, C. R., & Williams, L. M. (2011). Regional heterogeneity in limbic maturational changes: evidence from integrating cortical thickness, volumetric and diffusion tensor imaging measures. NeuroImage, 55(3), 868–879. doi:10.1016/j.neuroimage.2010.12.087. Hickie, I. B., Davenport, T. A., Naismith, S. L., & Scott, E. M. (2001). SPHERE: a national depression project. SPHERE National Secretariat. The Medical Journal of Australia, 175(Suppl), S4-5. Jacobs, D. M., Sano, M., Dooneief, G., Marder, K., Bell, K. L., & Stern, Y. (1995). Neuropsychological detection and characterization of preclinical Alzheimer’s disease. Neurology, 45(5), 957–962. Lerch, J. P., Pruessner, J. C., Zijdenbos, A., Hampel, H., Teipel, S. J., & Evans, A. C. (2005). Focal decline of cortical thickness in Alzheimer’s disease identified by computational neuroanatomy. Cerebral Cortex, 15(7), 995–1001. doi:10.1093/cercor/bhh200. Paul, R. H., Lawrence, J., Williams, L. M., Richard, C. C., Cooper, N., & Gordon, E. (2005). Preliminary validity of “integneuro”: a new computerized battery of neurocognitive tests. The International Journal of Neuroscience, 115(11), 1549–1567. doi:10.1080/00207450590957890. Rolls, E. T. (2000). Memory systems in the brain. Annual Review of Psychology, 51, 599–630. doi:10.1146/annurev.psych.51.1.599. Silverstein, S. M., Jaeger, J., Donovan-Lepore, A. M., Wilkniss, S. M., Savitz, A., Malinovsky, I., & Dent, G. (2010). A comparative study of the MATRICS and IntegNeuro cognitive assessment batteries. Journal of Clinical and Experimental Neuropsychology, 32(9), 937–952. doi:10.1080/13803391003596496. Squire, L. R., & Kowlton, B. J. (1999). The medial temporal lobe, the hippcomapus, and the memory systems of the brain. In M. S. Gazzaniga (Ed.), The new cognitive neurosciences. Cambridge: The MIT Press. Squire, L. R., & Zola-Morgan, S. (1991). The medial temporal lobe memory system. Science, 253(5026), 1380–1386. Troster, A. I., Butters, N., Salmon, D. P., Cullum, C. M., Jacobs, D., Brandt, J., & White, R. F. (1993). The diagnostic utility of savings scores: differentiating Alzheimer’s and huntington’s diseases with the logical memory and visual reproduction tests. Journal of Clinical and Experimental Neuropsychology, 15(5), 773–788. doi:10.1080/01688639308402595. Williams, L. M., Simms, E., Clark, C. R., Paul, R. H., Rowe, D., & Gordon, E. (2005). The test-retest reliability of a standardized neurocognitive and neurophysiological test battery: “neuromarker”. The International Journal of Neuroscience, 115(12), 1605–1630. doi:10.1080/00207450590958475.