Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-25T04:12:58.404Z Has data issue: false hasContentIssue false
  • English
  • Français

Examining the multifactorial nature of cognitive aging with covariance analysis of positron emission tomography data

Published online by Cambridge University Press:  01 November 2009

KAREN L. SIEDLECKI ,
CHRISTIAN G. HABECK ,
ADAM M. BRICKMAN ,
YUNGLIN GAZES  and
YAAKOV STERN
KAREN L. SIEDLECKI*
Affiliation:
Cognitive Neuroscience Division, Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, New York
CHRISTIAN G. HABECK
Affiliation:
Cognitive Neuroscience Division, Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, New York Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, New York
ADAM M. BRICKMAN
Affiliation:
Cognitive Neuroscience Division, Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, New York Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, New York
YUNGLIN GAZES
Affiliation:
Cognitive Neuroscience Division, Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, New York
YAAKOV STERN
Affiliation:
Cognitive Neuroscience Division, Taub Institute for Research in Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, New York Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, New York
*
*Correspondence and reprint requests to: Karen Siedlecki, 630 W. 168th Street, New York, New York 10032. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Research has indicated that there may be age-related and Alzheimer’s disease (AD) -related reductions in regional cerebral blood flow (rCBF) in the brain. This study explored differences in age- and AD-related rCBF patterns in the context of cognitive aging using a multivariate approach to the analysis of H215O PET data. First, an rCBF covariance pattern that distinguishes between a group of younger and older adults was identified. Individual subject’s expression of the identified age-related pattern was significantly correlated with their performance on tests of memory, even after controlling for the effect of age. This finding suggests that subject expression of the covariance pattern explained additional variation in performance on the memory tasks. The age-related covariance pattern was then compared to an AD-related covariance pattern. There was little evidence that the two covariance patterns were similar, and the age-related pattern did a poor job of differentiating between cognitively-healthy older adults and those with probable AD. The findings from this study are consistent with the multifactorial nature of cognitive aging. (JINS, 2009, 15, 973–981.)

Keywords

Alzheimer’s diseaseDementiaMemoryMultivariate analysisNeuroimagingScaled subprofile model
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 15 , Issue 6 , November 2009 , pp. 973 - 981
Copyright
Copyright © The International Neuropsychological Society 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Albert, M.S. (1997). The ageing brain: Normal and abnormal memory. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 352, 1703–1709.Google Scholar
Benson, D.F., Kuhl, D.E., Hawkins, R.A., Phelps, M.E., Cummings, J.L., & Tsai, S.Y. (1983). The fluorodeoxyglucose 18F scan in Alzheimer’s disease and multi-infarct dementia. Archives of Neurology, 40, 11–14.CrossRefGoogle ScholarPubMed
Bradley, K.M., O’Sullivan, V.T., Soper, N.D., Nagy, Z., King, E.M., Smith, A.D., et al. (2002). Cerebral perfusion in SPET correlated with Braak pathological stage in Alzheimer’s disease. Brain, 125, 1772–1781.CrossRefGoogle ScholarPubMed
Brayne, C., & Calloway, P. (1988). Normal ageing, impaired cognitive function, senile dementia of the Alzheimer’s type: A continuum? Lancet, 1, 1265–1267.CrossRefGoogle ScholarPubMed
Buckner, R.L. (2004). Memory and executive function in aging and AD: Multiple factors that cause decline and reserve factors that compensate. Neuron, 44, 195–208.CrossRefGoogle Scholar
Burnham, K.P., & Anderson, D.R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). New York: Spring-Verlag.Google Scholar
Bushke, H., & Fuld, P.A. (1974). Evaluating storage, retention, and retrieval in disordered memory and learning. Neurology, 24, 1019–1025.CrossRefGoogle Scholar
Christensen, H. (2001). What cognitive changes can be expected with normal ageing? Australian and New Zealand Journal of Psychiatry, 35, 768–775.CrossRefGoogle Scholar
Elias, M.F., Wolf, P.A., D’Agostino, R.B., Cobb, J., & White, L.R. (1993). Untreated blood pressure level is inversely related to cognitive functioning: The Framingham study. American Journal of Epidemiology, 138, 353–364.CrossRefGoogle ScholarPubMed
Folstein, M., Folstein, S., & McHugh, P. (1975). “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.CrossRefGoogle ScholarPubMed
Gabrieli, J.D. (1996). Memory systems analyses of mnemonic disorders in aging and age-related disease. Proceedings of the National Academy of Sciences of the United States of America, 93, 13534–13540.CrossRefGoogle Scholar
Head, D., Snyder, A.Z., Girton, L.E., Morris, J.C., & Buckner, R.L. (2005). Frontal-hippocampal double dissociation between normal aging and Alzheimer’s disease. Cerebral Cortex, 15, 732–739.CrossRefGoogle ScholarPubMed
Hedden, T., & Gabrieli, J.D. (2004). Insights into the ageing mind: A view from cognitive neuroscience. Nature, 5, 87–96.Google ScholarPubMed
Huppert, F.A. (1994). Memory function in dementia and normal aging- dimension or dichotomy? In Huppert, F.A., Brayne, C. & O’Connor, D.W. (Eds.), Dementia and normal aging (pp. 291–330). Cambridge: Cambridge University Press.Google Scholar
Jagust, W. (2004). Molecular neuroimaging in Alzheimer’s disease. NeuroRX, 1, 206–212.CrossRefGoogle ScholarPubMed
Johnson, K.A., Mueller, S.T., Walshe, T.M., English, R.J., & Holman, B.L. (1987). Cerebral perfusion imaging in Alzheimer’s disease: Use of single photon emission computed tomography and iofetamine hydrochloride I 123. Archives of Neurology, 44, 165–168.CrossRefGoogle ScholarPubMed
Klunk, W.E., Engler, H., Nordberg, A., Wang, Y., Blomqvist, G., Holt, D.P., et al. (2004). Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Annals of Neurology, 55, 306–309.CrossRefGoogle ScholarPubMed
Knopman, D., Boland, L.L., Mosley, T., Howard, G., Liao, D., Szklo, M., et al. (2001). Cardiovascular risk factors and cognitive decline in middle-aged adults. Neurology, 56, 42–48.CrossRefGoogle ScholarPubMed
Krausz, Y., Bonne, O., Gorfine, M., Karger, H., Lerer, B., & Chisin, R. (1998). Age related changes in brain perfusion of normal subjects detected by Tc-99m HMPAO SPECT. Neuroradiology, 40, 428–434.CrossRefGoogle Scholar
Luchsinger, J.A., & Mayeux, R. (2004). Cardiovascular risk factors and Alzheimer’s disease. Current Atherosclerosis Reports, 6, 261–266.CrossRefGoogle ScholarPubMed
Luchsinger, J.A., Reitz, C., Honig, L.S., Tang, M.X., Shea, S., & Mayeux, R. (2005). Aggregation of vascular risk factors and risk of incident Alzheimer disease. Neurology, 65, 545–551.CrossRefGoogle ScholarPubMed
McArdle, J.J., Ferrer-Caja, E., Hamagami, F., & Woodcock, R.W. (2002). Comparative longitudinal multilevel structural analyses of the growth and decline of multiple intellectual abilities over the life-span. Developmental Psychology, 38, 115–142.CrossRefGoogle ScholarPubMed
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E.M. (1984). Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA work group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s disease. Neurology, 34, 939–944.CrossRefGoogle ScholarPubMed
Meltzer, C.C., Cantwell, M.N., Greer, P.J., Ben-Eliezer, D., Smith, G., Frank, G., et al. (2000). Does cerebral blood flow decline in healthy aging? A PET study with partial-volume correction. The Journal of Nuclear Medicine, 41, 1842–1848.Google ScholarPubMed
Moeller, J.R., Ishikawa, T., Dhawan, V., Spetsieris, P., Mandel, F., Alexander, G.E., et al. (1996). The metabolic topography of normal aging. Journal of Cerebral Blood Flow and Metabolism, 16, 385–398.CrossRefGoogle Scholar
Nagamachi, S., Nishikawa, T., Ono, S., Ageta, M., Matsuo, T., Jinnouchi, S., et al. (1994). Regional cerebral blood flow in diabetic patients: Evaluation by N-isopropyl-123I-IMP with SPECT. Nuclear Medicine Communications, 15, 455–460.CrossRefGoogle ScholarPubMed
Nelson, H.E. (1982). National adult reading test. Test Manual. Windsor, UK: NFER- Nelson.Google Scholar
Nobili, F., Rodriguez, G., Marenco, S., De Carli, F., Gambaro, M., Castello, C., et al. (1993). Regional cerebral blood flow in chronic hypertension. A correlative study. Stroke, 24, 1148–1153.CrossRefGoogle ScholarPubMed
Ohnishi, T., Matsuda, H., Tabira, T., Asada, T., & Uno, M. (2001). Changes in brain morphology in Alzheimer’s disease and normal aging: Is Alzheimer’s disease an exaggerated aging process? AJNR American Journal of Neuroradiology, 22, 1680–1685.Google ScholarPubMed
Quirce, R., Carril, J.M., Jimenez-Bonilla, J.F., Amado, J.A., Gutierrez-Mendigucia, C., Banzo, I., et al. (1997). Semiquantitative assessment of cerebral blood flow with 99mTc- HMPAO SPET in type I diabetic patients with no clinical history of cerebrovascular disease. European Journal of Nuclear Medicine, 24, 1507–1513.CrossRefGoogle ScholarPubMed
Salthouse, T.A. (2004). What and when of cognitive aging. Current Directions in Psychological Science, 13, 140–144.CrossRefGoogle Scholar
Scarmeas, N., Habeck, C.G., Zarahn, E., Anderson, K.A., Park, A., Hilton, J., et al. (2004). Covariance PET patterns in early Alzheimer’s disease and subjects with cognitive impairment but no dementia: Utility in group discrimination and correlations with functional performance. Neuroimage, 23, 35–45.CrossRefGoogle Scholar
Shaw, T.G., Mortel, K.F., Meyer, J.S., Rogers, R.L., Hardenberg, J., & Cutaia, M.M. (1984). Cerebral blood flow changes in benign aging and cerebrovascular disease. Neurology, 34, 855–862.CrossRefGoogle ScholarPubMed
Stern, Y., Sano, M., Paulson, J., & Mayeux, R. (1987). Modified mini-mental state examination: Validity and reliability. Neurology, 3(Suppl. 1), 179.Google Scholar
Takahashi, K., Yamaguchi, S., Kobyashi, S., & Yamamoto, Y. (2005). Effects of aging on regional cerebral blood flow assessed by using Technetium Tc 99m Hexamethylpropyleneamine Oxime single-photon emission tomography with 3D stereotactic surface projection analysis. AJNR American Journal of Neuroradiology, 26, 2005–2009.Google ScholarPubMed
Tucker-Drob, E.M. (2009). Differentiation of cognitive abilities across the lifespan. Developmental Psychology, 45, 1097–1118.CrossRefGoogle Scholar
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15, 273–289.CrossRefGoogle ScholarPubMed
Wechsler, D. (1981). Wechsler Adult Intelligence Scale-Revised. New York: The Psychological Corporation.Google Scholar