Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-25T04:37:38.357Z Has data issue: false hasContentIssue false
  • English
  • Français

Computerized Cognitive Tests Are Associated with Biomarkers of Alzheimer’s Disease in Cognitively Normal Individuals 10 Years Prior

Published online by Cambridge University Press:  01 December 2016

Anja Soldan Open the ORCID record for Anja Soldan [Opens in a new window] ,
Corinne Pettigrew ,
Abhay Moghekar ,
Marilyn Albert  and
the BIOCARD Research Team
Anja Soldan*
Affiliation:
Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, Maryland
Corinne Pettigrew
Affiliation:
Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, Maryland
Abhay Moghekar
Affiliation:
Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, Maryland
Marilyn Albert
Affiliation:
Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, Maryland
*
Correspondence and reprint requests to: Anja Soldan, Division of Cognitive Neuroscience, 1620 McElderry Street, Reed Hall 1-West, Baltimore, MD 21205. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: Evidence suggests that Alzheimer’s disease (AD) biomarkers become abnormal many years before the emergence of clinical symptoms of AD, raising the possibility that biomarker levels measured in cognitively normal individuals would be associated with cognitive performance many years later. This study examined whether performance on computerized cognitive tests is associated with levels of cerebrospinal fluid (CSF) biomarkers of amyloid, tau, and phosphorylated tau (p-tau) obtained approximately 10 years earlier, when individuals were cognitively normal and primarily middle-aged. Methods: Individuals from the BIOCARD cohort (mean age at testing=69 years) were tested on two computerized tasks hypothesized to rely on brain regions affected by the early accumulation of AD pathology: (1) a Paired Associates Learning (PAL) task (n=67) and (2) a visual search task (n=86). Results: In regression analyses, poorer performance on the PAL task was associated with higher levels of CSF p-tau obtained years earlier, whereas worse performance in the visual search task was associated with lower levels of CSF Aβ1-42. Conclusions: These findings suggest that AD biomarker levels may be differentially predictive of specific cognitive functions many years later. In line with the pattern of early accumulation of AD pathology, the PAL task, hypothesized to rely on medial temporal lobe function, was associated with CSF p-tau, whereas the visual search task, hypothesized to rely on frontoparietal function, was associated with CSF amyloid. Studies using amyloid and tau PET imaging will be useful in examining these hypothesized relationships further. (JINS, 2016, 22, 968–977)

Keywords

Cerebrospinal fluidAlzheimer’s diseasePreclinicalAmyloidPhosphorylated tauMemoryVisual perceptionCognition
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 22 , Special Issue 10: Special Issue: Preclinical Prediction , November 2016 , pp. 968 - 977
Copyright
Copyright © The International Neuropsychological Society 2016 

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., Soldan, A., Gottesman, R., McKhann, G., Sacktor, N., Farrington, L., & Selnes, O. (2014). Cognitive changes preceding clinical symptom onset of mild cognitive impairment and relationship to ApoE genotype. Current Alzheimer Research, 11(8), 773–784.Google Scholar
Anderson, E.J., Mannan, S.K., Husain, M., Rees, G., Sumner, P., Mort, D.J., & Kennard, C. (2007). Involvement of prefrontal cortex in visual search. Experimental Brain Research, 180(2), 289–302.CrossRefGoogle ScholarPubMed
Anstey, K.J., Wood, J., Kerr, G., Caldwell, H., & Lord, S.R. (2009). Different cognitive profiles for single compared with recurrent fallers without dementia. Neuropsychology, 23(4), 500–508.Google Scholar
Aschenbrenner, A.J., Balota, D.A., Fagan, A.M., Duchek, J.M., Benzinger, T.L., & Morris, J.C. (2015). Alzheimer disease cerebrospinal fluid biomarkers moderate baseline differences and predict longitudinal change in attentional control and episodic memory composites in the adult children study. Journal of the International Neuropsychological Society, 21(8), 573–583.Google Scholar
Aschenbrenner, A.J., Balota, D.A., Tse, C.S., Fagan, A.M., Holtzman, D.M., Benzinger, T.L., &Morris, J.C. (2015). Alzheimer disease biomarkers, attentional control, and semantic memory retrieval: Synergistic and mediational effects of biomarkers on a sensitive cognitive measure in non-demented older adults. Neuropsychology, 29(3), 368–381.Google Scholar
Bennett, I.J., Barnes, K.A., Howard, J.H. Jr., & Howard, D.V. (2009). An abbreviated implicit spatial context learning task that yields greater learning. Behavioral Research Methods, 41(2), 391–395.Google Scholar
Bilgel, M., Jedynak, B., Wong, D.F., Resnick, S.M., & Prince, J.L. (2015). Temporal trajectory and progression score estimation from voxelwise longitudinal imaging measures: Application to amyloid imaging. Information Processing in Medical Imaging, 24, 424–436.CrossRefGoogle ScholarPubMed
Blacker, D., Lee, H., Muzikansky, A., Martin, E.C., Tanzi, R., McArdle, J.J., & Albert, M. (2007). Neuropsychological measures in normal individuals that predict subsequent cognitive decline. Archives of Neurology, 64(6), 862–871.CrossRefGoogle ScholarPubMed
Braak, H., Alafuzoff, I., Arzberger, T., Kretzschmar, H., & Del Tredici, K. (2006). Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathologica, 112(4), 389–404.Google Scholar
Braak, H., & Braak, E. (1991). Neuropathological stageing of Alzheimer-related changes. Acta Neuropathologica, 82(4), 239–259.Google Scholar
Chun, M.M., & Jiang, Y. (1998). Contextual cueing: Implicit learning and memory of visual context guides spatial attention. Cognitive Psychology, 36(1), 28–71.Google Scholar
Corbetta, M., & Shulman, G.L. (1998). Human cortical mechanisms of visual attention during orienting and search. Philosophical Transactions of the Royal Society B: Biological Sciences, 353(1373), 1353–1362.Google Scholar
Cummings, J.L., Mega, M., Gray, K., Rosenberg-Thompson, S., Carusi, D.A., & Gornbein, J. (1994). The Neuropsychiatric Inventory: Comprehensive assessment of psychopathology in dementia. Neurology, 44(12), 2308–2314.Google Scholar
de Jager, C.A., Milwain, E., & Budge, M. (2002). Early detection of isolated memory deficits in the elderly: The need for more sensitive neuropsychological tests. Psychological Medicine, 32(3), 483–491.CrossRefGoogle ScholarPubMed
de Rover, M., Pironti, V.A., McCabe, J.A., Acosta-Cabronero, J., Arana, F.S., Morein-Zamir, S., & Sahakian, B.J. (2011). Hippocampal dysfunction in patients with mild cognitive impairment: A functional neuroimaging study of a visuospatial paired associates learning task. Neuropsychologia, 49(7), 2060–2070.Google Scholar
Donner, T.H., Kettermann, A., Diesch, E., Ostendorf, F., Villringer, A., & Brandt, S.A. (2002). Visual feature and conjunction searches of equal difficulty engage only partially overlapping frontoparietal networks. Neuroimage, 15(1), 16–25.CrossRefGoogle ScholarPubMed
Egerhazi, A., Berecz, R., Bartok, E., & Degrell, I. (2007). Automated Neuropsychological Test Battery (CANTAB) in mild cognitive impairment and in Alzheimer’s disease. Progress in Neuropsychopharmacology & Biological Psychiatry, 31(3), 746–751.Google Scholar
Fagan, A.M., Head, D., Shah, A.R., Marcus, D., Mintun, M., Morris, J.C., & Holtzman, D.M. (2009). Decreased cerebrospinal fluid Abeta(42) correlates with brain atrophy in cognitively normal elderly. Annals of Neurology, 65(2), 176–183.Google Scholar
Fagan, A.M., Roe, C.M., Xiong, C., Mintun, M.A., Morris, J.C., & Holtzman, D.M. (2007). Cerebrospinal fluid tau/beta-amyloid(42) ratio as a prediction of cognitive decline in nondemented older adults. Archives of Neurology, 64(3), 343–349.Google Scholar
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.CrossRefGoogle ScholarPubMed
Glodzik, L., de Santi, S., Tsui, W.H., Mosconi, L., Zinkowski, R., Pirraglia, E., & de Leon, M.J. (2011). Phosphorylated tau 231, memory decline and medial temporal atrophy in normal elders. Neurobiology of Aging, 32(12), 2131–2141.Google Scholar
Glodzik, L., Mosconi, L., Tsui, W., de Santi, S., Zinkowski, R., Pirraglia, E., & de Leon, M.J. (2012). Alzheimer’s disease markers, hypertension, and gray matter damage in normal elderly. Neurobiology of Aging, 33(7), 1215–1227.CrossRefGoogle ScholarPubMed
Heitz, R.P. (2014). The speed-accuracy tradeoff: History, physiology, methodology, and behavior. Frontiers in Neuroscience, 8, 150.Google Scholar
Howieson, D.B., Carlson, N.E., Moore, M.M., Wasserman, D., Abendroth, C.D., Payne-Murphy, J., &Kaye, J.A. (2008). Trajectory of mild cognitive impairment onset. Journal of the International Neuropsychological Society, 14(2), 192–198.Google Scholar
Hughes, C.P., Berg, L., Danziger, W.L., Coben, L.A., & Martin, R.L. (1982). A new clinical scale for the staging of dementia. British Journal of Psychiatry, 140, 566–572.CrossRefGoogle ScholarPubMed
Insel, P.S., Mattsson, N., Mackin, R.S., Kornak, J., Nosheny, R., Tosun-Turgut, D., & Weiner, M.W. (2015). Biomarkers and cognitive endpoints to optimize trials in Alzheimer’s disease. Annals of Clinical and Translational Neurology, 2(5), 534–547.Google Scholar
Jack, C.R. Jr., Knopman, D.S., Jagust, W.J., Petersen, R.C., Weiner, M.W., Aisen, P.S., & Trojanowski, J.Q. (2013). Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurology, 12(2), 207–216.Google Scholar
Junkkila, J., Oja, S., Laine, M., & Karrasch, M. (2012). Applicability of the CANTAB-PAL computerized memory test in identifying amnestic mild cognitive impairment and Alzheimer’s disease. Dementia and Geriatric Cognitive Disorders, 34(2), 83–89.Google Scholar
Lenehan, M.E., Summers, M.J., Saunders, N.L., Summers, J.J., & Vickers, J.C. (2016). Does the Cambridge Automated Neuropsychological Test Battery (CANTAB) distinguish between cognitive domains in healthy older adults? Assessment, 15, 192–195.Google Scholar
Li, G., Millard, S.P., Peskind, E.R., Zhang, J., Yu, C.E., Leverenz, J.B., & Montine, T.J. (2014). Cross-sectional and longitudinal relationships between cerebrospinal fluid biomarkers and cognitive function in people without cognitive impairment from across the adult life span. JAMA Neurology, 71(6), 742–751.Google Scholar
Manelis, A., & Reder, L.M. (2012). Procedural learning and associative memory mechanisms contribute to contextual cueing: Evidence from fMRI and eye-tracking. Learning & Memory, 19(11), 527–534.Google Scholar
Mattsson, N., Insel, P., Nosheny, R., Trojanowski, J.Q., Shaw, L.M., Jack, C.R. Jr., & Weiner, M. (2014). Effects of cerebrospinal fluid proteins on brain atrophy rates in cognitively healthy older adults. Neurobiology of Aging, 35(3), 614–622.Google Scholar
Mattsson, N., Insel, P.S., Nosheny, R., Tosun, D., Trojanowski, J.Q., Shaw, L.M., & Weiner, M.W. (2014). Emerging beta-amyloid pathology and accelerated cortical atrophy. JAMA Neurology, 71(6), 725–734.Google Scholar
McLaughlin, P.M., Borrie, M.J., & Murtha, S.J. (2010). Shifting efficacy, distribution of attention and controlled processing in two subtypes of mild cognitive impairment: Response time performance and intraindividual variability on a visual search task. Neurocase, 16(5), 408–417.Google Scholar
Moghekar, A., Goh, J., Li, M., Albert, M., & O’Brien, R.J. (2012). Cerebrospinal fluid Abeta and tau level fluctuation in an older clinical cohort. Archives of Neurology, 69(2), 246–250.CrossRefGoogle Scholar
Moghekar, A., Li, S., Lu, Y., Li, M., Wang, M.C., Albert, M., &O’Brien, R. (2013). CSF biomarker changes precede symptom onset of mild cognitive impairment. Neurology, 81(20), 1753–1758.CrossRefGoogle ScholarPubMed
Morris, J.C. (1993). The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology, 43(11), 2412–2414.CrossRefGoogle ScholarPubMed
Muller-Oehring, E.M., Schulte, T., Rohlfing, T., Pfefferbaum, A., & Sullivan, E.V. (2013). Visual search and the aging brain: Discerning the effects of age-related brain volume shrinkage on alertness, feature binding, and attentional control. Neuropsychology, 27(1), 48–59.Google Scholar
Pettigrew, C., Soldan, A., Moghekar, A., Wang, M.C., Gross, A.L., O’Brien, R., & Albert, M. (2015). Relationship between cerebrospinal fluid biomarkers of Alzheimer’s disease and cognition in cognitively normal older adults. Neuropsychologia, 78, 63–72.Google Scholar
Reitan, R.M. (1958). Validity of the trail making test as an indicator of organic brain damage. Perceptual and Motor Skills, 8, 271–276.Google Scholar
Rey, A. (1941). L’examen psychologique dans les cas d’encephalopathie traumatique. Archives de Psychologie, 28, 286–340.Google Scholar
Roe, C.M., Fagan, A.M., Grant, E.A., Marcus, D.S., Benzinger, T.L., Mintun, M.A., & Morris, J.C. (2011). Cerebrospinal fluid biomarkers, education, brain volume, and future cognition. Archives of Neurology, 68(9), 1145–1151.Google Scholar
Rosler, A., Mapstone, M.E., Hays, A.K., Mesulam, M.M., Rademaker, A., Gitelman, D.R., &Weintraub, S. (2000). Alterations of visual search strategy in Alzheimer’s disease and aging. Neuropsychology, 14(3), 398–408.Google Scholar
Sahakian, B.J., Morris, R.G., Evenden, J.L., Heald, A., Levy, R., Philpot, M., & Robbins, T.W. (1988). A comparative study of visuospatial memory and learning in Alzheimer-type dementia and Parkinson’s disease. Brain, 111(Pt 3), 695–718.Google Scholar
Soldan, A., Pettigrew, C., Li, S., Wang, M.C., Moghekar, A., Selnes, O.A., & O’Brien, R. (2013). Relationship of cognitive reserve and cerebrospinal fluid biomarkers to the emergence of clinical symptoms in preclinical Alzheimer’s disease. Neurobiology of Aging, 34(12), 2827–2834.Google Scholar
Soldan, A., Pettigrew, C., Wang, M.C., Moghekar, A., O’Brien, R., Selnes, O., & Albert, M. (in press). Hypothetical preclinical Alzheimer’s disease groups and longitudinal cognitive change. JAMA Neurology.Google Scholar
Sperling, R.A., Aisen, P.S., Beckett, L.A., Bennett, D.A., Craft, S., Fagan, A.M., & Phelps, C.H. (2011). Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 280–292.Google Scholar
Steenland, K., Zhao, L., Goldstein, F., Cellar, J., & Lah, J. (2014). Biomarkers for predicting cognitive decline in those with normal cognition. Journal of Alzheimer’s Disease, 40(3), 587–594.Google Scholar
Stricker, N.H., Dodge, H.H., Dowling, N.M., Han, S.D., Erosheva, E.A., & Jagust, W.J. (2012). CSF biomarker associations with change in hippocampal volume and precuneus thickness: Implications for the Alzheimer’s pathological cascade. Brain Imaging and Behavior, 6(4), 599–609.Google Scholar
Sutphen, C.L., Jasielec, M.S., Shah, A.R., Macy, E.M., Xiong, C., Vlassenko, A.G., & Fagan, A.M. (2015). Longitudinal cerebrospinal fluid biomarker changes in preclinical Alzheimer disease during middle age. JAMA Neurology, 72(9), 1029–1042.Google Scholar
Tales, A., Bayer, A.J., Haworth, J., Snowden, R.J., Philips, M., & Wilcock, G. (2011). Visual search in mild cognitive impairment: A longitudinal study. Journal of Alzheimer’s Disease, 24(1), 151–160.Google Scholar
Tales, A., Haworth, J., Nelson, S., Snowden, R.J., & Wilcock, G. (2005). Abnormal visual search in mild cognitive impairment and Alzheimer’s disease. Neurocase, 11(1), 80–84.Google Scholar
Thal, D.R., Rub, U., Orantes, M., & Braak, H. (2002). Phases of A beta-deposition in the human brain and its relevance for the development of AD. Neurology, 58(12), 1791–1800.Google Scholar
Tosun, D., Schuff, N., Shaw, L.M., Trojanowski, J.Q., & Weiner, M.W. (2011). Relationship between CSF biomarkers of Alzheimer’s disease and rates of regional cortical thinning in ADNI data. Journal of Alzheimer’s Disease, 26(Suppl. 3), 77–90.Google Scholar
Viskontas, I.V., Boxer, A.L., Fesenko, J., Matlin, A., Heuer, H.W., Mirsky, J., &Miller, B.L. (2011). Visual search patterns in semantic dementia show paradoxical facilitation of binding processes. Neuropsychologia, 49(3), 468–478.Google Scholar
Vos, S.J., Xiong, C., Visser, P.J., Jasielec, M.S., Hassenstab, J., Grant, E.A., & Fagan, A.M. (2013). Preclinical Alzheimer’s disease and its outcome: A longitudinal cohort study. The Lancet Neurology, 12(10), 957–965.Google Scholar
Whelan, R. (2008). Effective analysis of reaction time data. The Psychological Record, 58, 475–482.Google Scholar
Wilson, R.S., Leurgans, S.E., Boyle, P.A., & Bennett, D.A. (2011). Cognitive decline in prodromal Alzheimer disease and mild cognitive impairment. Archives of Neurology, 68(3), 351–356.Google Scholar
Yesavage, J.A., Brink, T.L., Rose, T.L., Lum, O., Huang, V., Adey, M., & Leirer, V.O. (1982). Development and validation of a geriatric depression screening scale: A preliminary report. Journal of Psychiatric Research, 17(1), 37–49.Google Scholar
Yotter, R.A., Doshi, J., Clark, V., Sojkova, J., Zhou, Y., Wong, D.F., & Davatzikos, C. (2013). Memory decline shows stronger associations with estimated spatial patterns of amyloid deposition progression than total amyloid burden. Neurobiology of Aging, 34(12), 2835–2842.Google Scholar