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Alzheimer Disease Cerebrospinal Fluid Biomarkers Moderate Baseline Differences and Predict Longitudinal Change in Attentional Control and Episodic Memory Composites in the Adult Children Study

Published online by Cambridge University Press:  29 September 2015

Andrew J. Aschenbrenner ,
David A. Balota ,
Anne M. Fagan ,
Janet M. Duchek ,
Tammie L.S. Benzinger  and
John C. Morris
Andrew J. Aschenbrenner*
Affiliation:
Department of Psychology, Washington University in St. Louis, St. Louis, Missouri
David A. Balota
Affiliation:
Department of Psychology, Washington University in St. Louis, St. Louis, Missouri Department of Neurology, Washington University in St. Louis, St. Louis, Missouri
Anne M. Fagan
Affiliation:
Department of Neurology, Washington University in St. Louis, St. Louis, Missouri The Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, Missouri
Janet M. Duchek
Affiliation:
Department of Psychology, Washington University in St. Louis, St. Louis, Missouri
Tammie L.S. Benzinger
Affiliation:
The Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, Missouri Department of Radiology, Washington University in St. Louis, St. Louis, Missouri Department of Neurological Surgery, Washington University in St. Louis, St. Louis, Missouri
John C. Morris
Affiliation:
Department of Neurology, Washington University in St. Louis, St. Louis, Missouri The Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, Missouri
*
Correspondence and reprint requests to: Andrew Aschenbrenner, Washington University, Department of Psychology, St. Louis, MO, 63130. E-mail: a.aschenbrenner@wustl.edu
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Abstract

Cognitive measures that are sensitive to biological markers of Alzheimer disease (AD) pathology are needed to (a) facilitate preclinical staging, (b) identify individuals who are at the highest risk for developing clinical symptoms, and (c) serve as endpoints for evaluating the efficacy of interventions. The present study assesses the utility of two cognitive composite scores of attentional control and episodic memory as markers for preclinical AD pathology in a group of cognitively normal older adults (N=238), as part of the Adult Children Study. All participants were given a baseline cognitive assessment and follow-up assessments every 3 years over an 8-year period, as well as a lumbar puncture within 2 years of the initial assessment to collect cerebrospinal fluid (CSF) and amyloid tracer Pittsburgh compound-B scan for amyloid imaging. Results indicated that attentional control was correlated with levels of Aβ42 at the initial assessment whereas episodic memory was not. Longitudinally, individuals with high CSF tau exhibited a decline in both attention and episodic memory over the course of the study. These results indicate that measures of attentional control and episodic memory can be used to evaluate cognitive decline in preclinical AD and provide support that CSF tau may be a key mechanism driving longitudinal cognitive change. (JINS, 2015, 21, 573–583)

Keywords

AttentionEpisodic memoryLongitudinal studyAlzheimer diseaseBiological markersAmyloid
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 21 , Issue 8 , September 2015 , pp. 573 - 583
Copyright
Copyright © The International Neuropsychological Society 2015 

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References

Aizenstein, H.J., Nebes, R.D., Saxton, J.A., Price, J.C., Mathis, C.A., Tsopelas, N.D., & Klunk, W.E. (2008). Frequent amyloid deposition without significant cognitive impairment among the elderly. Archives of Neurology, 65, 1509–1517. doi:10.1001/archneur.65.11.1509CrossRefGoogle ScholarPubMed
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, 368–381.CrossRefGoogle ScholarPubMed
Balota, D.A., & Duchek, J.M. (2015). Attention, variability, and biomarkers in Alzheimer’s disease. In Remembering: Attributions, processes, and control in human memory (pp. 285–303). New York: Psychology Press.Google Scholar
Balota, D.A., & Faust, M. (2001). Attention in Dementia of the Alzheimers Type. In F. Boller & S. Cappa (Eds.), Handbook of Neuropsychology (2nd ed., Vol. 6, pp. 51–80). New York: Elsevier Science.Google Scholar
Balota, D.A., Tse, C.S., Hutchison, K.A., Spieler, D.H., Duchek, J.M., & Morris, J.C. (2010). Predicting conversion to dementia of the Alzheimer’s type in a healthy control sample: The power of errors in Stroop color naming. Psychology and Aging, 25, 208–218. doi:10.1037/a0017474CrossRefGoogle Scholar
Banich, M.T., Milham, M.P., Atchley, R., Cohen, N.J., Webb, A., Wszalek, T., & Magin, R. (2000). fMri studies of Stroop tasks reveal unique roles of anterior and posterior brain systems in attentional selection. Journal of Cognitive Neuroscience, 12, 988–1000.CrossRefGoogle ScholarPubMed
Bateman, R.J., Xiong, C., Benzinger, T.L., Fagan, A.M., Goate, A., Fox, N.C., &Morris, J.C. (2012). Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. New England Journal of Medicine, 367, 795–804. doi:10.1056/NEJMoa1202753CrossRefGoogle ScholarPubMed
Bates, D., Maechler, M., Bolker, B., & Walker, S. (2014). lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1–7. Retrieved from http://CRAN.R-project.org/packagelme4Google Scholar
Bertens, D., Knol, D.L., Scheltens, P., & Visser, P.J. (2015). Temporal evolution of biomarkers and cognitive markers in the asymptomatic, MCI, and dementia stage of Alzheimer’s disease. Alzheimer’s & Dementia, 11, 511–522. doi:10.1016/j.jalz.2014.05.1754CrossRefGoogle ScholarPubMed
Buckner, R.L., Snyder, A.Z., Shannon, B.J., LaRossa, G., Sachs, R., Fotenos, A.F., & Mintun, M.A. (2005). Molecular, structural, and functional characterization of Alzheimer’s disease: Evidence for a relationship between default activity, amyloid, and memory. Journal of Neuroscience, 25(34), 7709–7717. doi:10.1523/JNEUROSCI.2177-05.2005CrossRefGoogle ScholarPubMed
Castel, A.D., Balota, D.A., Hutchison, K.A., Logan, J.M., & Yap, M.J. (2007). Spatial attention and response control in healthy younger and older adults and individuals with Alzheimer’s disease: Evidence for disproportionate selection impairments in the Simon task. Neuropsychology, 21, 170–182. doi:10.1037/0894-4105.21.2.170CrossRefGoogle ScholarPubMed
Craig-Schapiro, R., Perrin, R.J., Roe, C.M., Xiong, C., Carter, D., Cairns, N.J., & Holtzman, D.M. (2010). YKL-40: A novel prognostic fluid biomarker for preclinical Alzheimer’s disease. Biological Psychiatry, 68(10), 903–912. doi:10.1016/j.biopsych.2010.08.025CrossRefGoogle ScholarPubMed
Craik, F.I.M., & Lockhart, R.S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11, 671–684.CrossRefGoogle Scholar
Davison, A.C., & Hinkley, D.V. (1997). Bootstrap methods and their application. New York: Cambridge University Press.CrossRefGoogle Scholar
Doraiswamy, P.M., Sperling, R.A., Coleman, R.E., Johnson, K.A., Reiman, E.M., Davis, M.D., & Pontecorvo, M.J. (2012). Amyloid-assessed by florbetapir F 18 PET and 18-month cognitive decline: A multicenter study. Neurology, 79, 1636–1644. doi:10.1212/WNL.0b013e3182661f74CrossRefGoogle ScholarPubMed
Duchek, J.M., Balota, D.A., Thomas, J.B., Snyder, A.Z., Rich, P., Benzinger, T.L., & Ances, B.M. (2013). Relationship between Stroop performance and resting state functional connectivity in cognitively normal older adults. Neuropsychology, 27(5), 516–528. doi:10.1037/a0033402CrossRefGoogle ScholarPubMed
Duchek, J.M., Balota, D.A., Tse, C.S., Holtzman, D.M., Fagan, A.M., & Goate, A.M. (2009). The utility of intraindividual variability in selective attention tasks as an early marker for Alzheimer’s disease. Neuropsychology, 23(6), 746–758. doi:10.1037/a0016583CrossRefGoogle ScholarPubMed
Ellis, K.A., Lim, Y.Y., Harrington, K., Ames, D., Bush, A.I., Darby, D., & Maruff, P. (2013). Decline in cognitive function over 18 months in healthy older adults with high amyloid-β. Journal of Alzheimer’s Disease, 34(4), 861–871.CrossRefGoogle ScholarPubMed
Ewers, M., Insel, P., Jagust, W.J., Shaw, L., Trojanowski, J.Q., Aisen, P., & Weiner, M.W. (2012). CSF biomarker and PIB-PET-derived beta-amyloid signature predicts metabolic, gray matter, and cognitive changes in nondemented subjects. Cerebral Cortex, 22(9), 1993–2004. doi:10.1093/cercor/bhr271CrossRefGoogle ScholarPubMed
Fagan, A.M., Mintun, M.A., Shah, A.R., Aldea, P., Roe, C.M., Mach, R.H., & Holtzman, D.M. (2009). Cerebrospinal fluid tau and ptau181 increase with cortical amyloid deposition in cognitively normal individuals: Implications for future clinical trials of Alzheimer’s disease. EMBO Molecular Medicine, 1, 371–380. doi:10.1002/emmm.200900048CrossRefGoogle ScholarPubMed
Fagan, A.M., Roe, C.M., Xiong, C., Mintun, M.A., Morris, J.C., & Holtzman, D.M. (2007). Cerebrospinal fluid tau/beta-amyloid42 ratio as a prediction of cognitive decline in nondemented older adults. Archives of Neurology, 64, 343–349. doi:10.1001/archneur.64.3.noc60123CrossRefGoogle Scholar
Fischl, B. (2004). Automatically parcellating the human cerebral cortex. Cerebral Cortex, 14, 11–22.CrossRefGoogle ScholarPubMed
Galvin, J.E., Powlishta, K.K., Wilkins, K., McKeel, D.W., Xiong, C., Grant, E., & Morris, J.C. (2005). Predictors of preclinical Alzheimer disease and dementia: A clinicopathologic study. Archives of Neurology, 62, 758–765.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. doi:10.1016/j.neurobiolaging.2009.12.026CrossRefGoogle ScholarPubMed
Gordon, B.A., Zacks, J.M., Blazey, T., Benzinger, T.L., Morris, J.C., Fagan, A.M., & Balota, D.A. (2015). Task-evoked fMRI changes in attention networks are associated with preclinical Alzheimer’s disease biomarkers. Neurobiology of Aging, 36, 1771–1779. doi:10.1016/j.neurobiolaging.2015.01.019CrossRefGoogle ScholarPubMed
Grober, E., Buschke, H., Crystal, H., Bang, S., & Dresner, R. (1988). Screening for demenita by memory testing. Neurology, 38, 900–903.CrossRefGoogle Scholar
Hedden, T., Oh, H., Younger, A.P., & Patel, T.A. (2013). Meta-analysis of amyloid-cognition relations in cognitively normal older adults. Neurology, 80, 1341–1348.CrossRefGoogle ScholarPubMed
Kane, M.J., & Engle, R.W. (2002). The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective. Psychonomic Bulletin & Review, 9(4), 637–671. doi:10.3758/BF03196323CrossRefGoogle ScholarPubMed
Klunk, W.E., Engler, H., Nordberg, A., Wang, Y., Blomqvist, G., Holt, D.P., & Långström, B. (2004). Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Annals of Neurology, 55(3), 306–319. doi:10.1002/ana.20009CrossRefGoogle ScholarPubMed
Kuznetsova, A., Brockhoff, P.B., & Christensen, R.H.B. (2014). lmerT-est: Tests for random and fixed effects for linear mixed effects models (lmer objects of lme4 package). R Package Version 2.0 –11. Retrieved from http://CRAN.R-project.org/packagelmerTestGoogle Scholar
Landau, S.M., Mintun, M.A., Joshi, A.D., Koeppe, R.A., Petersen, R.C., Aisen, P.S.,& Jagust, W.J. (2012). Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Annals of Neurology, 72(4), 578–586. doi:10.1002/ana.23650CrossRefGoogle ScholarPubMed
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. doi:10.1001/jamaneurol.2014.445CrossRefGoogle ScholarPubMed
Li, G., Sokal, I., Quinn, J.F., Leverenz, J.B., Brodey, M., Schellenberg, G.D., & Montine, T.J. (2007). CSF tau/Aβ42 ratio for increased risk of mild cognitive impairment A follow-up study. Neurology, 69, 631–639.CrossRefGoogle ScholarPubMed
Lim, Y.Y., Pietrzak, R.H., Ellis, K.A., Jaeger, J., Harrington, K., Ashwood, T., & Maruff, P. (2013). Rapid decline in episodic memory in healthy older adults with high amyloid-β. Journal of Alzheimer’s Disease, 33, 675–679. doi:10.3233/JAD-2012-121516CrossRefGoogle ScholarPubMed
Lo, R.Y., Hubbard, A.E., Shaw, L.M., Trojanowski, J.Q., Peterson, R.C., Aisen, P.S., & Jagust, W.J. (2011). Longitudinal change of biomarkers in cognitive decline. Archives of Neurology, 68(10), 1257–1266. doi:10.1001/archneurol.2011.123CrossRefGoogle ScholarPubMed
Mattsson, N., Andreasson, U., Persson, S., Arai, H., Batish, S.D., Bernardini, S., & Blennow, K. (2011). The Alzheimer’s Association external quality control program for cerebrospinal fluid biomarkers. Alzheimer’s & Dementia, 7, 386–395e6. doi:10.1016/j.jalz.2011.05.2243CrossRefGoogle Scholar
McCabe, D.P., Roediger, H.L., McDaniel, M.A., Balota, D.A., & Hambrick, D.Z. (2010). The relationship between working memory capacity and executive functioning: Evidence for a common executive attention construct. Neuropsychology, 24(2), 222–243. doi:10.1037/a0017619CrossRefGoogle ScholarPubMed
Mintun, M.A., LaRossa, G.N., Sheline, Y.I., Dence, C.S., Lee, S.Y., Mach, R.H., & Morris, J.C. (2006). [11C]PIB in a nondemented population: Potential antecedent marker of Alzheimer disease. Neurology, 67, 446–452. doi:10.1212/01.wnl.0000228230.26044.a4CrossRefGoogle Scholar
Morris, J.C. (1993). The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology, 43, 2412–2414.CrossRefGoogle ScholarPubMed
Morris, J.C., McKeel, D.W., Storandt, M., Rubin, E.H., Price, J.L., Grant, E.A., & Berg, L. (1991). Very mild Alzheimer’s disease: Informant-based clinical, psychometric, and pathologic distinction from normal aging. Neurology, 41, 469–478.CrossRefGoogle ScholarPubMed
Mungas, D., Tractenberg, R., Schneider, J.A., Crane, P.K., & Bennett, D.A. (2014). A 2-process model for neuropathology of Alzheimer’s disease. Neurobiology of Aging, 35, 301–308. doi:10.1016/j.neurobiolaging.2013.08.007CrossRefGoogle ScholarPubMed
Nebes, R.D., Snitz, B.E., Cohen, A.D., Aizenstein, H.J., Saxton, J.A., Halligan, E.M., & Klunk, W.E. (2013). Cognitive aging in persons with minimal amyloid-β and white matter hyperintensities. Neuropsychologia, 51, 2202–2209. doi:10.1016/j.neuropsychologia.2013.07.017Google ScholarPubMed
Perry, R.J., & Hodges, J.R. (1999). Attention and executive deficits in Alzheimer’s disease: A critical review. Brain: A Journal of Neurology, 122, 383–404.CrossRefGoogle ScholarPubMed
Price, J.L., McKeel, D.W., Buckles, V.D., Roe, C.M., Xiong, C., Grundman, M., & Morris, J.C. (2009). Neuropathology of nondemented aging: Presumptive evidence for preclinical Alzheimer disease. Neurobiology of Aging, 30, 1026–1036. doi:10.1016/j.neurobiolaging.2009.04.002CrossRefGoogle ScholarPubMed
Resnick, S.M., Sojkova, J., Zhou, Y., An, Y., Ye, W., Holt, D.P., & Wong, D.F. (2010). Longitudinal cognitive decline is associated with fibrillar amyloid-beta measured by [11C]PiB. Neurology, 74, 807–815.CrossRefGoogle ScholarPubMed
Rodrigue, K.M., Kennedy, K.M., Devous, M.D., Rieck, J.R., Hebrank, A.C., Diaz-Arrasita, R., & Park, D.C. (2012). B-Amyloid burden in healthy aging: Regional distribution and cognitive consequences. Neurology, 78, 387–395.CrossRefGoogle Scholar
Roe, C.M., Fagan, A.M., Grant, E.A., Hassenstab, J., Moulder, K.L., Dreyfus, D.M., & Morris, J.C. (2013). Amyloid imaging and CSF biomarkers in predicting cognitive impairment up to 7.5 years later. Neurology, 80, 1784–1791.Google ScholarPubMed
Rolstad, S., Berg, A.I., Bjerke, M., Johansson, B., Zetterberg, H., & Wallin, A. (2013). Cerebrospinal fluid biomarkers mirror rate of cognitive decline. Journal of Alzheimer’s Disease, 34, 949–956. doi:10.3233/JAD-121960CrossRefGoogle ScholarPubMed
Simon, J.R. (1969). Reactions toward the source of stimulation. Journal of Experimental Psychology, 81, 174–176.CrossRefGoogle ScholarPubMed
Snijders, T., & Bosker, R. (1999). Multilevel Analysis: An introduction to basic and advanced multilevel modeling. Thousand Oaks, CA: SAGE Publications.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, 280–292. doi:10.1016/j.jalz.2011.03.003CrossRefGoogle ScholarPubMed
Sperling, R.A., Johnson, K.A., Doraiswamy, P.M., Reiman, E.M., Fleisher, A.S., Sabbagh, M.N., & Pontecorvo, M.J. (2013). Amyloid deposition detected with florbetapir F 18 (18F-AV-45) is related to lower episodic memory performance in clinically normal older individuals. Neurobiology of Aging, 34, 822–831. doi:10.1016/j.neurobiolaging.2012.06.014Google ScholarPubMed
Spieler, D.H., Balota, D.A., & Faust, M.E. (1996). Stroop performance in healthy younger and older adults and in individuals with dementia of the Alzheimer’s type. Journal of Experimental Psychology: Human Perception and Performance, 22, 461–479.Google ScholarPubMed
Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47, 2015–2028. doi:10.1016/j.neuropsychologia.2009.03.004CrossRefGoogle ScholarPubMed
Stomrud, E., Hansson, O., Zetterberg, H., Blennow, K., Minthon, L., & Londos, E. (2010). Correlation of longitudinal cerebrospinal fluid biomarkers with cognitive decline in healthy older adults. Archives of Neurology, 67(2), 217–223.CrossRefGoogle ScholarPubMed
Storandt, M., Head, D., Fagan, A.M., Holtzman, D.M., & Morris, J.C. (2012). Toward a multifactorial model of Alzheimer disease. Neurobiology of Aging, 33, 2262–2271. doi:10.1016/j.neurobiolaging.2011.11.029CrossRefGoogle Scholar
Storandt, M., & Hill, R.D. (1989). Very mild senile dementia of the Alzheimer type II: Psychometric test performance. Archives of Neurology, 46, 383–386.CrossRefGoogle ScholarPubMed
Storandt, M., Mintun, M.A., Head, D., & Morris, J.C. (2009). Cognitive decline and brain volume loss as signatures of cerebral amyloid-β peptide deposition identified with Pittsburgh compound B. Archives of Neurology, 66, 1476–1481. doi:10.1001/archneurol.2009.272Google Scholar
Stroop, J.R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643–662. doi:10.1037/ h0054651CrossRefGoogle Scholar
Su, Y., D’Angelo, G.M., Vlassenko, A.G., Zhou, G., Snyder, A.Z., Marcus, D.S., & Benzinger, T.L. (2013). Quantitative analysis of PiB-PET with Freesurfer ROIs. PLoS One, 8, e73377.CrossRefGoogle ScholarPubMed
Tse, C.S., Balota, D.A., Yap, M.J., Duchek, J.M., & McCabe, D.P. (2010). Effects of healthy aging and early stage dementia of the Alzheimer’s type on components of response time distributions in three attention tasks. Neuropsychology, 24, 300–315. doi:10.1037/a0018274CrossRefGoogle ScholarPubMed
Twamley, E.W., Ropacki, S.A., & Bondi, M.W. (2006). Neuropsychological and neuroimaging changes in preclinical Alzheimer’s disease. Journal of the International Neuropsychological Society, 12, 707–735.CrossRefGoogle ScholarPubMed
Vanderhasselt, M.A., De Raedt, R., & Baeken, C. (2009). Dorsolateral prefrontal cortex and Stroop performance: Tackling the lateralization. Psychonomic Bulletin & Review, 16(3), 609–612. doi:10.3758/PBR.16.3.609CrossRefGoogle ScholarPubMed
Vemuri, P., Wiste, H.J., Weigand, S.D., Shaw, L.M., Trojanowski, J.Q., Weiner, M.W., & Jack, C.R. (2009). MRI and CSF biomarkers in normal, MCI, and AD subjects Diagnostic discrimination and cognitive correlations. Neurology, 73, 287–293. doi:10.1212/WNL.0b013e3181af79e5CrossRefGoogle Scholar
Villemagne, V.L., Pike, K.E., Chételat, G., Ellis, K.A., Mulligan, R.S., Bourgeat, P., & Rowe, C.C. (2011). Longitudinal assessment of Aβ and cognition in aging and Alzheimer disease. Annals of Neurology, 69(1), 181–192. doi:10.1002/ana.22248CrossRefGoogle ScholarPubMed
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, 957–965.CrossRefGoogle ScholarPubMed
Wechsler, D. (1987). Weschler Memory Scale: Administration and Scoring manual (3rd ed.). San Antonio, TX: Psychological Corporation.Google Scholar
Wechsler, D., & Stone, C.P. (1973). Manual: Wechsler Memory Scale. New York: Psychological Corporation.Google Scholar
Xiong, C., Roe, C.M., Buckles, V., Fagan, A., Holtzman, D., Balota, D., & Morris, J.C. (2011). Role of family history for Alzheimer biomarker abnormalities in the Adult Children Study. Archives of Neurology, 68(10), 1313–1319.CrossRefGoogle ScholarPubMed