Role of Family History for Alzheimer Biomarker Abnormalities in the Adult Children Study | Dementia and Cognitive Impairment | JAMA Neurology | JAMA Network
[Skip to Navigation]
Sign In
Figure. Cerebrospinal fluid (CSF) Aβ42 as functions of age and family history (FH). See Table 1 for the sample size and demographic characteristics of the subgroup.

Figure. Cerebrospinal fluid (CSF) Aβ42 as functions of age and family history (FH). See Table 1 for the sample size and demographic characteristics of the subgroup.

Table 1. Characteristics of the ACS Cohort at Baseline
Table 1. Characteristics of the ACS Cohort at Baseline
Table 2. Estimated Slope (per Year of Age) for MCBP and CSF Tau on Middle- and Older-Age Individuals as a Function of FH and APOE4a
Table 2. Estimated Slope (per Year of Age) for MCBP and CSF Tau on Middle- and Older-Age Individuals as a Function of FH and APOE4a
Original Contributions
Oct 2011

Role of Family History for Alzheimer Biomarker Abnormalities in the Adult Children Study

Author Affiliations

Author Affiliations: Charles F. and Joanne Knight Alzheimer's Disease Research Center (Drs Xiong, Roe, Buckles, Fagan, Holtzman, Balota, Duchek, Storandt, Mintun, Grant, Snyder, Head, Benzinger, Mettenburg, Csernansky, and Morris), and Departments of Neurology (Drs Roe, Buckles, Fagan, Holtzman, Storandt, and Morris), Pathology and Immunology (Dr Morris), Physical Therapy (Dr Morris), Occupational Therapy (Dr Morris), Biostatistics (Drs Xiong and Grant), Psychology (Drs Balota, Duchek, Storandt, and Head), Radiology (Drs Mintun, Snyder, Benzinger, Mettenburg, and Head), and Development Biology (Dr Holtzman), Washington University School of Medicine, St Louis, Missouri; and Departments of Psychiatry and Behavioral Sciences, Stone Institute of Psychiatry, and Northwestern Memorial Hospital, Chicago, Illinois (Dr Csernansky).

Arch Neurol. 2011;68(10):1313-1319. doi:10.1001/archneurol.2011.208

Objective To assess whether family history (FH) of Alzheimer disease (AD) alone influences AD biomarker abnormalities.

Design Adult Children Study.

Setting Washington University's Charles F. and Joanne Knight Alzheimer's Disease Research Center.

Participants A total of 269 cognitively normal middle- to older-aged individuals with and without an FH for AD.

Main Outcome Measures Clinical and cognitive measures, magnetic resonance imaging–based brain volumes, diffusion tensor imaging–based white matter microstructure, cerebrospinal fluid biomarkers, and molecular imaging of cerebral fibrillar amyloid with positron emission tomography using the [11C] benzothiazole tracer, Pittsburgh compound B.

Results A positive FH for AD was associated with an age-related decrease of cerebrospinal fluid Aβ42; the ε4 allele of apolipoprotein E (APOE4) did not alter this effect. Age-adjusted cerebrospinal fluid Aβ42 was decreased for individuals with APOE4 compared with the level for those without, and the decrease was larger for individuals with a positive FH compared with the decrease for those without. The variation of cerebrospinal fluid tau and Pittsburgh compound B mean cortical binding potential increased by age. For individuals younger than 55, an age-related increase in mean cortical binding potential was associated with APOE4 but not FH. For individuals older than 55, a positive FH and a positive APOE4 implied the fastest age-related increase in mean cortical binding potential. A positive FH was associated with decreased fractional anisotropy from diffusion tensor imaging in the genu and splenium of the corpus callosum.

Conclusion Independent of APOE4, FH is associated with age-related change of several cerebrospinal fluid, Pittsburgh compound B, and diffusion tensor imaging biomarkers in cognitively normal middle- to older-aged individuals, suggesting that non- APOE susceptibility genes for AD influence AD biomarkers.

Recent advances suggest that Alzheimer disease (AD) has a lengthy period in which cerebral lesions gradually accumulate in the absence of symptoms, eventually causing sufficient synaptic and neuronal damage to result in symptomatic AD.1-5 Since 2005, Antecedent Biomarkers for AD: The Adult Children Study (ACS) has enrolled a cohort of cognitively normal 43- to 76-year-old individuals in an extensive study of biomarkers for AD before its symptomatic stages. In addition to clinical and cognitive measures, a broad spectrum of candidate antecedent biomarkers for AD were assessed, including magnetic resonance imaging (MRI)–based brain volumes, diffusion tensor imaging–based measures of white matter microstructure, cerebrospinal fluid (CSF), and molecular imaging of cerebral fibrillar amyloid with positron emission tomography (PET) using the [11C] benzothiazole tracer, Pittsburgh compound B (PIB). Because the ACS cohort is cognitively normal, changes in these well-established biomarkers for AD likely represent the insidious pathogenesis of AD well before the development of symptoms, ie, during the preclinical stage of AD.

The ACS cohort is stratified by family history (FH) for AD to genetically enrich the participants at risk of AD. Therefore, analysis on FH and biomarkers allows a linkage of biomarker abnormality to susceptibility genes for AD, especially non–apolipoprotein E (APOE) genes (ie, PICALM, CR1, and CLU discovered from recent genome-wide association studies6,7) if the effect of FH is independent of APOE. Whereas several studies reported changes in isolated biomarkers with relatively small samples of elderly normal individuals with an FH of AD8-10 or the ε4 allele of APOE (APOE4) (OMIM +107741),11 the ACS facilitates a comprehensive analysis of both FH and APOE for a wide array of candidate antecedent biomarkers in cognitively normal individuals of middle to older age (43-76 years).

The objective of this study was to assess whether FH alone conveys AD risk beyond that of APOE4 by examining the influence of FH for AD, both together and independent of APOE4, on biomarker abnormalities using the baseline data of the ACS.


As of October 2009, the ACS cohort included 269 community-living volunteers from the greater St Louis metropolitan area. Recruitment primarily was through word of mouth and personal inquiries. A positive FH for AD was defined as at least 1 biological parent with age at onset for dementia of the Alzheimer type (DAT) of less than 80 years, and a negative FH was defined as both biological parents living to age 70 or longer without DAT. If a parent living to age 70 without DAT later developed DAT by age 80, the participant was reassigned to the positive FH group. About one-third of the participants were children of parents enrolled in longitudinal studies of the Washington University Alzheimer's Disease Research Center. Eligibility criteria for the ACS were age 45 to 75 (2 early enrollees were age 43 and 76 years), availability of an informant who knew the participant well, normal cognition (defined as Clinical Dementia Rating12 of 0), and willingness in principle to complete all procedures. Comorbid conditions, including depressive features short of major affective disorder, were acceptable if the patient was clinically stable at time of enrollment. Exclusion criteria included conditions such as end-stage cancer or end-stage renal disease that would preclude longitudinal participation and/or confound cognitive assessment. Another exclusion criterion was membership in families with a dominantly inherited pattern of AD and/or a known causative mutation for AD. The Washington University Human Research Protection Office approved the study.

Clinical and cognitive assessments

The primary clinical assessment protocol was that of the National Alzheimer Coordinating Center Uniform Data Set.13 Additional clinical information, such as an assessment of autobiographical memory using events in which the participant recently engaged,14 was obtained. The standard definitions and criteria15 of the Uniform Data Set for detection of dementia and its differential diagnosis were used.16 The presence or absence of dementia and, when present, its severity were operationalized with the Clinical Dementia Rating.12The Clinical Dementia Rating is based on the judgment of an experienced physician, with informant information and examination of the participant, as to whether the individual performs accustomed activities at his or her previously attained level17 and was completed independently of neuropsychological test results. The Clinical Dementia Rating is highly reliable18-20 and sensitive and accurate for even very mild cognitive decline caused by AD.17,21,22 The clinical assessment takes 90 minutes to complete.

Participants completed psychometric testing 1 to 2 weeks after they received the clinical assessment. The 5 cognitive domains assessed in the 2-hour battery were episodic memory (Wechsler Memory Scale–III Logical Memory I and II, Verbal Paired Associates I,23 and Free and Cued Selective Reminding24), working memory (Wechsler Memory Scale–III Letter-Number Sequencing, Auditory Consonant Trigrams,25 and Reading Span26), semantic knowledge (Wechsler Adult Intelligence Scale–III Similarities and Information27 and Animal Naming28), executive function and attention (Trailmaking Test A and B,29 Simon Task,30 and Switching Task31), and visuospatial ability (Wechsler Adult Intelligence Scale–III Block Design,27 Benton Line Orientation,32 and Woodcock-Johnson Visual Relations33). The clinical and cognitive assessments are obtained at baseline and every 3 years thereafter except for participants aged 65 years or older, for whom they are obtained annually.

Csf collection and analysis

Cerebrospinal fluid (20-30 mL) was collected by routine lumbar puncture, free from any blood contamination, in polypropylene tubes at 8:00 AM after overnight fasting, as previously described.34 The samples were analyzed for total tau, tau phosphorylated at threonine-181 (ptau181), and Aβ1-42by commercial enzyme-linked immunosorbent assay (Innotest; Innogenetics, Ghent, Belgium). Cerebrospinal fluid Aβ40 was assayed by enzyme-linked immunosorbent assay as previously described.35 For all CSF measures, samples were continuously kept on ice, and assays were performed on sample aliquots after a single thaw following initial freezing.

Image acquisition and processing

Magnetic resonance imaging scans were obtained on either a Sonata 1.5T, Vision 1.5T, or Trio 3.0T scanner (Siemens Corporation, Malvern, Pennsylvania). Structural MRI processing steps have been described in detail previously36-38 and include motion correction, averaging across scans, atlas transformation, and inhomogeneity correction. Regional volumes were obtained via the Freesurfer image analysis suite, version 4.1.0 (Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts). The regions of interest are detailed elsewhere.37 A comparison between the Vision 1.5T and Trio 3.0T scanners of Freesurfer-derived volumes yielded an average intraclass correlation of 0.81.37 Analysis was performed on adjusted volumetric measures after regressing for the effect of scanner platform.

Diffusion tensor images were collected at 3T for the assessment of white matter microstructural integrity (2×2×2-mm voxels, repetition time = 9900 ms, echo time = 102 ms, flip angle = 90°, and b-values scaled up to 1400 maximum, using 23 diffusion encoding directions). Data were collected in two 6-minute runs. Quantitative images of mean diffusivity, fractional anisotropy, and axial and radial diffusivity for regions of interest were computed as previously described.39

Positron emission tomography PIB imaging and analysis procedures have been reported elsewhere.40 Brain PET imaging was conducted using a Siemens 961 HR ECAT PET scanner or a Siemens 962 HR+ ECAT PET scanner (both, Control Technology, Inc, Knoxville, Kentucky). Radiochemical synthesis of [11C]PIB was performed in accordance with the published literature.41 After a transmission scan to measure attenuation, approximately 12 mCi of [11C]PIB was administered intravenously simultaneously with initiation of a 60-minute dynamic PET scan in 3-dimensional mode (septa retracted; twenty-four 5-second frames, nine 20-second frames, and ten 1-minute frames). The measured attenuation factors, scatter correction, and a ramp filter were used to reconstruct the dynamic PET images. Analysis of PIB images was performed for specific regions of interest as detailed previously.40,42 The cerebellum was chosen as the reference region because of little specific binding of PIB.41 The Logan analysis42 yields a tracer distribution to volume ratio, resulting in estimates of the binding potential for each region of interest, as follows: binding potential equals distribution to volume ratio minus 1.40 The binding potential values from the prefrontal cortex, gyrus rectus, lateral temporal, and precuneus regions of interest were averaged to calculate a mean cortical binding potential (MCBP).40

Attentional assessment

A 2-hour attentional battery was administered separately from the psychometric testing. The attentional control tasks were computation span,43 letter rotation span,44 Stroop,45 and a process dissociation task.45 The 2 span tasks involved participants making a series of true/false judgments, with the working memory component being to remember in order the parts of the stimulus across the judgments. The Stroop is a computerized color naming task, which includes 60 trials for the congruent (eg, BLUE in BLUE), neutral (eg, DEEP in BLUE), and incongruent (eg, RED in BLUE) conditions. The process dissociation task places recollection in direct conflict with familiarity via opposition procedures during retrieval.46 A Consonant–Vowel/Odd–Even Switching task47 was also administered.


We extracted DNA from peripheral blood samples using standard procedures. Apolipoprotein E genotyping was performed as previously described.48

Statistical analysis

The analysis was done on ACS baseline data. Each marker was analyzed as a function of age, FH (yes or no), and APOE4 genotype (ε4 allele present or absent) by the analysis of covariance.49 The interactive effects among these 3 risk factors were first tested and reported if confirmed. Otherwise, independent effects of each risk factor were reported. Preliminary analysis suggested differential variances as a function of age (ie, younger vs older than 55), which were tested and then accommodated in the associational analyses with FH and APOE4 if confirmed. The software PROC MIXED/SAS50 (SAS Institute, Cary, North Carolina) was used to implement these analyses. The Satterthwaite approximation49 was used to estimate the denominator df in the approximate F or t tests.


Table 1 presents the demographic characteristics of the entire sample and subgroups with each modality of assessments. All 269 participants completed baseline clinical and psychometric assessments. Two hundred seventeen participants (80.7%) had a lumbar puncture to obtain CSF, 206 (76.6%) completed PET PIB, 147 (54.6%) had an MRI, and 232 (86.2%) completed the attentional battery. One hundred eight participants (40.1%) completed all baseline procedures (clinical, psychometric, attention, lumbar puncture, MRI, and PET PIB).

As shown in the Figure, the mean (SE) level of CSF Aβ42 decreased significantly with age at a rate of −7.76 (2.14) pg/mL per year (P < .001) in those with a positive FH but not in those without (P = .35). The presence of an APOE4 allele did not alter the effect of FH on the age-related decrease in CSF Aβ42 (P = .50). Those with an ε4 allele had lower levels of age-adjusted CSF Aβ42 compared with the corresponding level in those without (P < .001), and the decrease was larger if FH was positive compared with the decrease if FH was negative (F1,209 = 5.29; P = .02). Sensitivity analyses with multiple imputations51 on CSF Aβ42 confirmed these findings.

The variance increased among individuals aged 55 or older compared with that of the younger age group for CSF tau (χ21 = 9.71; P = .002) and MCBP (χ21 = 98.35; P < .001). Table 2 presents the estimated slope (per year of age) for MCBP and CSF tau on younger (<55 years) and older individuals (≥55 years) as a function of FH and APOE4. No significant effect of FH or APOE4 was found for CSF tau on the age-related rate of change, but individuals with a positive FH had a higher level of CSF tau than those otherwise (F1,152 = 4.60; P = .03) at age 55. For individuals younger than 55, MCBP increased by age at a significantly faster pace for individuals with APOE4 compared with the pace for those without APOE4 (F1,62.4 = 4.72; P = .03), eventually leading to a higher level of MCBP for those with APOE4 compared with the level for those without (P = .01). For individuals older than 55, a trend (P = .09) was found to suggest a faster age-related increase of MCBP for individuals with APOE4 compared with the increase for those without APOE4. Individuals with a positive FH and a positive APOE4 had the largest age-related increase of MCBP (P < .001).

Brain volumes as determined by MRI decreased with age, but the difference was not statistically significant by FH (total cerebral brain volume: F1,132 = 0.90, P = .34; right hippocampal volume: F1,139 = 1.85, P = .18; left hippocampal volume: F1,139 = 0.31, P = .58).

From a subsample of 165 participants who had diffusion tensor imaging data, the age-adjusted mean level of fractional anisotropy was lower for individuals with an FH of AD compared with the level for those without an FH of AD in the genu (F1,142 = 3.91; P = .05) and in the splenium (F1,142 = 4.12; P = .04) of the corpus callosum. In the gyrus rectus, individuals with APOE4 had a lower level of fractional anisotropy (F1,142 = 4.75; P = .03) and a higher level of radial diffusivity (F1,142 = 4.3; P = .04) than those without APOE4. The age-related increase in radial diffusivity in the precuneus was faster if FH was positive, compared with the increase if FH was negative, only among individuals with APOE4 (F1,142 = 4.67; P = .03). The mean (SE) performance level of auditory consonant trigrams decreased significantly with age at the rate of −0.411 (0.125) per year (P = .001) for those with a positive FH but not for those with a negative FH (P = .52).

One hundred fifteen and 52 participants reported their mother's and their father's age of onset of DAT, respectively. An earlier mother's age of onset was correlated with a larger reaction time difference between pure blocks and switched blocks of trials from the Consonant–Vowel/Odd–Even Switching task46 (Spearman r = −0.21; P = .04), and an earlier father's age of onset was correlated with poorer performance in Wechsler Adult Intelligence Scale–III Similarities (r = 0.44; P = .01).

Exploratory correlational analyses across the entire modalities of biomarkers confirmed those previously reported in the literature.34,52,53 Significant correlations between MCBP and CSF biomarkers (tau r = 0.22, ptau181r = 0.19, and CSF Aβ42 r = −0.41) were observed in the entire ACS cohort. Some of these are potentially modulated by age but not by FH. In the younger cohort (age <55 years), MCBP was not significantly correlated with CSF biomarkers or brain volumes. In the older cohort (age ≥55 years), however, MCBP was significantly correlated with CSF biomarkers (tau r = 0.24, ptau181r = 0.22, and Aβ42 r = −0.53). Furthermore, CSF and imaging biomarkers were correlated with Stroop performance in the younger sample in only 2 occasions (Aβ42 with greater interference in reaction times r = −0.28, and MCBP with Simon coefficient of variation r = 0.31). In the older sample, however, CSF and imaging biomarkers were correlated with poorer performance across many attention measures (eg, Aβ42 with task switching coefficient of variation r = −0.22 and interference errors r = −0.20; MCBP with task switching coefficient of variation r = 0.19, interference RT r = 0.17 and errors r = 0.19, incongruent errors r = 0.19, and Simon coefficient of variation r = 0.17). Brain volumetric measures were also correlated with attentional and working memory measures (eg, total cerebral brain volume with rotation span r = 0.22, and left hippocampal volume with rotation span r = 0.35) in the older sample. Because of a large number of correlations assessed across all modalities of markers, these findings were subject to a higher false-positive rate (>5%). Therefore, they were preliminary and will serve only to generate scientific hypotheses that need to be critically tested in future studies.

Analyses were repeated on the subgroup of individuals who completed all procedures. The findings were consistent with the reported statistics, although a severe loss of statistical power resulted in losses of statistical significance.


Family history for AD as a risk factor for AD and cognitive decline has been well documented,54-56 many times jointly with APOE4 genotypes. Several studies reported reduced gray matter volume8 and brain glucose metabolism9 as well as increased semantic memory activation10 in healthy individuals with a maternal history of AD. These reports, however, focused mostly on a small number of biomarkers assessed on the elderly population aged 65 or older. We reported the influence of FH for AD for a wide array of candidate antecedent biomarkers in the ACS cohort of cognitively normal middle- to older-aged individuals (aged 43-76 years). In addition to clinical and cognitive measures, we analyzed MRI-based brain volumes, diffusion tensor imaging–based estimates of white matter microstructure, biofluid assays, and molecular imaging of fibrillar amyloid measure with PET PIB.

No difference was found on cognitive and clinical measures as a function of FH of AD among cognitively normal ACS individuals. The only possible exception comes from the performance on the auditory consonant trigrams,25 and the difference is no longer significant after multiplicity adjustment.

Family history for AD, however, was associated with several CSF and imaging biomarkers in the cognitively normal ACS cohort, suggesting their potential role as antecedent biomarkers of AD. These findings support the design of the ACS that genetically enriched the sample of cognitively normal individuals at risk of AD by FH and are consistent with a recently reported meta-analysis of diffusion tensor imaging.57 The current results point to the likelihood of non- APOE susceptibility genes for AD, consistent with recent reports of multiple risk genes (PICALM, CR1, and CLU) of AD from several genome-wide association studies.6,7

Together, our data across a wide spectrum of biomarkers on a cohort of cognitively normal middle- to older-aged individuals, albeit cross-sectional, suggest that AD has a lengthy period during which cerebral lesions gradually accumulate in the absence of symptoms (ie, preclinical AD). We expect that eventually these lesions cause sufficient synaptic and neuronal damage to result in symptomatic AD. More specifically, among cognitively normal middle- to older-aged individuals, age-related changes in brain Aβ42 metabolism as well as local microstructural characteristics of water diffusion in certain brain regions are influenced by FH of AD, suggesting that they are likely early events in AD pathogenesis. Significant disruptions in CSF tau and ptau181 metabolism, reflecting other changes in the structural integrity of axonal tracts, likely occur after brain Aβ42 initially aggregates and then increases as amyloid accumulates. Interestingly, CSF Aβ42 and MCBP are correlated with several of the attentional measures. These correlations suggest that antecedent biomarker changes likely have a deleterious effect on neuronal and attentional integrity.

For CSF tau and MCBP, we also observed increased variability as a function of age, which was further accompanied by an accelerated age-related increase. This finding, although cross-sectional, is consistent with several longitudinal studies in which an accelerated cognitive decline58,59 preceding the onset of DAT was reported. Whereas cognitive changes might be later events in the neurodegenerative sequence before the onset of DAT, changes in CSF and PIB biomarkers have the potential to capture the earliest possible antecedent events.

This study has several limitations. First, the ACS is an observational study on a convenience sample. Unobserved factors could contribute to the differences of subgroups with each modality of measures. The interpretation of the findings thus has the standard limitations of observational studies. Second, a lack of longitudinal data on biomarkers prevents us from understanding the cascade of early events in AD pathogenesis. The ongoing longitudinal follow-up of clinical and biomarker measures on the ACS cohort will provide much more insight into the preclinical progression of AD.

Back to top
Article Information

Correspondence: Chengjie Xiong, Department of Biostatistics, Washington University School of Medicine, 660 S Euclid Ave, Campus Box 8067, St Louis, MO 63110 (

Accepted for Publication: March 15, 2011.

Author Contributions: Dr Xiong had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Xiong, Roe, Buckles, Mintun, Csernansky, and Morris. Acquisition of data: Fagan, Balota, Duchek, Storandt, Mintun, Grant, Head, Benzinger, Mettenburg, and Morris. Analysis and interpretation of data: Xiong, Roe, Buckles, Holtzman, Duchek, Snyder, Benzinger, and Morris. Drafting of the manuscript: Xiong, Balota, and Snyder. Critical revision of the manuscript for important intellectual content: Xiong, Roe, Buckles, Fagan, Holtzman, Duchek, Storandt, Mintun, Grant, Head, Benzinger, Mettenburg, Csernansky, and Morris. Statistical analysis: Xiong, Roe, and Duchek. Obtained funding: Xiong, Balota, Storandt, Head, and Morris. Administrative, technical, and material support: Buckles, Fagan, Holtzman, Duchek, Mintun, Grant, Snyder, Csernansky, and Morris. Study supervision: Storandt, Mintun, Benzinger, and Morris.

Financial Disclosure: Dr Holtzman reports that he receives research funding from Eli Lilly and Company, AstraZeneca, Pfizer, and C2N Diagnostics through Washington University; he is a member of the scientific advisory board for En Vivo, Satori, and C2N Diagnostics; and he is a consultant for Innogenetics. Dr Mintun reports that he is the chief medical officer for Avid Radiopharmaceuticals.

Funding/Support: This study was supported by grant P01 AG026276 from the National Institute on Aging (Dr Morris), the American Roentgen Ray Scholar Award (Dr Benzinger), and grant P50 AG05681 from the Charles F. and Joanne Knight Alzheimer's Research Initiative of the Washington University Alzheimer's Disease Research Center (Dr Morris).

Additional Contributions: We thank the Genetics Core (Alison Goate, DPhil, Core Leader) of the Alzheimer's Disease Research Center for the APOE data.

Price JL, Morris JC. Tangles and plaques in nondemented aging and “preclinical” Alzheimer's disease.  Ann Neurol. 1999;45(3):358-36810072051PubMedGoogle ScholarCrossref
Morris JC, Price AL. Pathologic correlates of nondemented aging, mild cognitive impairment, and early-stage Alzheimer's disease.  J Mol Neurosci. 2001;17(2):101-11811816784PubMedGoogle ScholarCrossref
Price JL, McKeel DW Jr, Buckles VD,  et al.  Neuropathology of nondemented aging: presumptive evidence for preclinical Alzheimer disease.  Neurobiol Aging. 2009;30(7):1026-103619376612PubMedGoogle ScholarCrossref
Bennett DA, Schneider JA, Arvanitakis Z,  et al.  Neuropathology of older persons without cognitive impairment from two community-based studies.  Neurology. 2006;66(12):1837-184416801647PubMedGoogle ScholarCrossref
Jack CR Jr, Knopman DS, Jagust WJ,  et al.  Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade.  Lancet Neurol. 2010;9(1):119-12820083042PubMedGoogle ScholarCrossref
Harold D, Abraham R, Hollingworth P,  et al.  Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease.  Nat Genet. 2009;41(10):1088-109319734902PubMedGoogle ScholarCrossref
Lambert J-C, Heath S, Even G,  et al; European Alzheimer's Disease Initiative Investigators.  Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer's disease.  Nat Genet. 2009;41(10):1094-109919734903PubMedGoogle ScholarCrossref
Honea RA, Swerdlow RH, Vidoni ED, Goodwin J, Burns JM. Reduced gray matter volume in normal adults with a maternal family history of Alzheimer disease.  Neurology. 2010;74(2):113-12020065246PubMedGoogle ScholarCrossref
Mosconi L, Mistur R, Switalski R,  et al.  Declining brain glucose metabolism in normal individuals with a maternal history of Alzheimer disease.  Neurology. 2009;72(6):513-52019005175PubMedGoogle ScholarCrossref
Seidenberg M, Guidotti L, Nielson KA,  et al.  Semantic memory activation in individuals at risk for developing Alzheimer disease.  Neurology. 2009;73(8):612-62019704080PubMedGoogle ScholarCrossref
Morris JC, Roe CM, Xiong C,  et al.  APOE predicts Aβ but not tau Alzheimer pathology in cognitively normal aging.  Ann Neurol. 2010;67(1):122-13120186853PubMedGoogle ScholarCrossref
Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules.  Neurology. 1993;43(11):2412-24148232972PubMedGoogle ScholarCrossref
Coats M, Morris JC. Antecedent biomarkers of Alzheimer's disease: the Adult Children Study.  J Geriatr Psychiatry Neurol. 2005;18(4):242-24416306247PubMedGoogle ScholarCrossref
Cortese MJ, Balota DA, Sergent-Marshall SD, Buckner RL, Gold BT. Consistency and regularity in past-tense verb generation in healthy ageing, Alzheimer's disease, and semantic dementia.  Cogn Neuropsychol. 2006;23(6):856-87621049357PubMedGoogle ScholarCrossref
American Psychiatric Association.  Diagnostic and Statistical Manual of Mental Disorders. ed 4. Washington, DC: American Psychiatric Association; 1994
Morris JC, Weintraub S, Chui HC,  et al.  The Uniform Data Set (UDS): clinical and cognitive variables and descriptive data from Alzheimer disease centers.  Alzheimer Dis Assoc Disord. 2006;20(4):210-21617132964PubMedGoogle ScholarCrossref
Storandt M, Grant EA, Miller JP, Morris JC. Longitudinal course and neuropathologic outcomes in original vs revised MCI and in pre-MCI.  Neurology. 2006;67(3):467-47316894109PubMedGoogle ScholarCrossref
Burke WJ, Miller JP, Rubin EH,  et al.  Reliability of the Washington University Clinical Dementia Rating.  Arch Neurol. 1988;45(1):31-323337672PubMedGoogle ScholarCrossref
McCulla MM, Coats M, Van Fleet N, Duchek J, Grant E, Morris JC. Reliability of clinical nurse specialists in the staging of dementia.  Arch Neurol. 1989;46(11):1210-12112818255PubMedGoogle ScholarCrossref
Morris JC, Ernesto C, Schafer K,  et al.  Clinical dementia rating training and reliability in multicenter studies: the Alzheimer's Disease Cooperative Study experience.  Neurology. 1997;48(6):1508-15109191756PubMedGoogle ScholarCrossref
Morris JC, Storandt M, Miller JP,  et al.  Mild cognitive impairment represents early-stage Alzheimer disease.  Arch Neurol. 2001;58(3):397-40511255443PubMedGoogle ScholarCrossref
Morris JC, McKeel DW Jr, Storandt M,  et al.  Very mild Alzheimer's disease: informant-based clinical, psychometric, and pathologic distinction from normal aging.  Neurology. 1991;41(4):469-4782011242PubMedGoogle ScholarCrossref
Wechsler D. Wechsler Memory Scale–Third Edition: Administration and Scoring Manual. San Antonio, TX: Psychological Corporation; 1977
Grober E, Buschke H, Crystal H, Bang S, Dresner R. Screening for dementia by memory testing.  Neurology. 1988;38(6):900-9033368071PubMedGoogle ScholarCrossref
Peterson LR, Peterson MJ. Short-term retention of individual verbal items.  J Exp Psychol. 1959;58:193-19814432252PubMedGoogle ScholarCrossref
Daneman M, Carpenter PA. Individual differences in working memory and reading.  J Verbal Learn Verbal Behav. 1980;19:450-466Google ScholarCrossref
Wechsler D. Wechsler Adult Intelligence Scale–Third Edition: Administration and Scoring Manual. San Antonio, TX: Psychological Corporation; 1997
Goodglass H, Kaplan E. Boston Diagnostic Aphasia Examination Booklet, III: Oral Expression, J: Animal Naming (Fluency in Controlled Association). Philadelphia, PA: Lea & Febiger; 1983
Armitage SG. An analysis of certain psychological tests used for the evaluation of brain injury.  Psychol Monogr. 1945;60(1, Whole No. 177):1-48Google Scholar
Simon JR. Reactions toward the source of stimulation.  J Exp Psychol. 1969;81(1):174-1765812172PubMedGoogle ScholarCrossref
Rogers RD, Monsell S. Costs of a predictable switch between simple cognitive tasks.  J Exp Psychol Gen. 1995;124:207-231Google ScholarCrossref
Benton AL,. Hamsher KdeS, Varney NR, Spreen O. Contributions to Neuropsychological Assessment: A Clinical Manual. New York, NY: Oxford University Press; 1983
Woodcock RW, McGrew KS, Mather N. Examiner's Manual: Woodcock-Johnson III Tests of Cognitive Abilities. Itasca, IL: Riverside Publishing; 2001
Fagan AM, Mintun MA, Mach RH,  et al.  Inverse relation between in vivo amyloid imaging load and cerebrospinal fluid Aβ42 in humans.  Ann Neurol. 2006;59(3):512-51916372280PubMedGoogle ScholarCrossref
Cirrito JR, May PC, O’Dell MA,  et al.  In vivo assessment of brain interstitial fluid with microdialysis reveals plaque-associated changes in amyloid-β metabolism and half-life.  J Neurosci. 2003;23(26):8844-885314523085PubMedGoogle Scholar
Buckner RL, Head D, Parker J,  et al.  A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume.  Neuroimage. 2004;23(2):724-73815488422PubMedGoogle ScholarCrossref
Storandt M, Mintun MA, Head D, Morris JC. Cognitive decline and brain volume loss as signatures of cerebral amyloid-β peptide deposition identified with Pittsburgh compound B: cognitive decline associated with Aβ deposition.  Arch Neurol. 2009;66(12):1476-148120008651PubMedGoogle ScholarCrossref
Talairach J, Tournoux P. Co-Planar Stereotaxic Atlas of the Human Brain.  Stuttgart, Germany: Thieme Medical Publishers Inc; 1988
Head D, Buckner RL, Shimony JS,  et al.  Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: evidence from diffusion tensor imaging.  Cereb Cortex. 2004;14(4):410-42315028645PubMedGoogle ScholarCrossref
Mintun MA, Larossa GN, Sheline YI,  et al.  [11C]PIB in a nondemented population: potential antecedent marker of Alzheimer disease.  Neurology. 2006;67(3):446-45216894106PubMedGoogle ScholarCrossref
Mathis CA, Wang Y, Holt DP, Huang GF, Debnath ML, Klunk WE. Synthesis and evaluation of 11C-labeled 6-substituted 2-arylbenzothiazoles as amyloid imaging agents.  J Med Chem. 2003;46(13):2740-275412801237PubMedGoogle ScholarCrossref
Logan J, Fowler JS, Volkow ND, Wang GJ, Ding YS, Alexoff DL. Distribution volume ratios without blood sampling from graphical analysis of PET data.  J Cereb Blood Flow Metab. 1996;16(5):834-8408784228PubMedGoogle ScholarCrossref
Salthouse TA, Babcock RL. Decomposing adult age differences in working memory.  Dev Psychol. 1991;27(5):763-776Google ScholarCrossref
McCabe DP, Roediger HL, McDaniel MA, Balota DA, Hambrick DZ. The relationship between working memory capacity and executive functioning: evidence for an executive attention construct.  Neuropsychology. 2010;24(2):222-24320230116PubMedGoogle ScholarCrossref
Tse CS, Balota DA, Moynan SC, Duchek JM, Jacoby LL. The utility of placing recollection in opposition to familiarity in early discrimination of healthy aging and very mild dementia of the Alzheimer's type (DAT).  Neuropsychology. 2010;24(1):49-6720063946PubMedGoogle ScholarCrossref
Jacoby LL. A process dissociation framework: separating automatic from intentional uses of memory.  J Mem Lang. 1991;30:513-541Google ScholarCrossref
Tse CS, Balota DA, Yap MJ, Duchek JM, McCabe DP. Effects of healthy aging and early stage dementia of the Alzheimer's type on components of response time distributions in three attention tasks.  Neuropsychology. 2010;24(3):300-31520438208PubMedGoogle ScholarCrossref
Talbot C, Lendon C, Craddock N, Shears S, Morris JC, Goate A. Protection against Alzheimer's disease with apoE epsilon 2.  Lancet. 1994;343(8910):1432-14337910910PubMedGoogle ScholarCrossref
Milliken GA, Johnson DE. Analysis of Covariance. New York, NY: Chapman & Hall/CRC; 2001. Analysis of Messy Data. Vol 3
Littell RC, Milliken GA, Stroup WW,  et al.  SAS System for Mixed Models. Cary, NC: SAS Institute Inc; 1996
Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York, NY: John Wiley & Sons, Inc; 1987
Fagan AM, Mintun MA, Shah AR,  et al.  Cerebrospinal fluid tau and ptau181 increase with cortical amyloid deposition in cognitively normal individuals: implications for future clinical trials of Alzheimer's disease.  EMBO Mol Med. 2009;1(8-9):371-38020049742PubMedGoogle ScholarCrossref
Fagan AM, Head D, Shah AR,  et al.  Decreased cerebrospinal fluid Aβ42 correlates with brain atrophy in cognitively normal elderly.  Ann Neurol. 2009;65(2):176-18319260027PubMedGoogle ScholarCrossref
Jayadev S, Steinbart EJ, Chi Y-Y, Kukull WA, Schellenberg GD, Bird TD. Conjugal Alzheimer disease: risk in children when both parents have Alzheimer disease.  Arch Neurol. 2008;65(3):373-37818332250PubMedGoogle ScholarCrossref
Huang W, Qiu C, von Strauss E, Winblad B, Fratiglioni L. APOE genotype, family history of dementia, and Alzheimer disease risk: a 6-year follow-up study.  Arch Neurol. 2004;61(12):1930-193415596614PubMedGoogle ScholarCrossref
Hayden KM, Zandi PP, West NA,  et al; Cache County Study Group.  Effects of family history and apolipoprotein E ε4 status on cognitive decline in the absence of Alzheimer dementia: the Cache County Study.  Arch Neurol. 2009;66(11):1378-138319901170PubMedGoogle ScholarCrossref
Sexton CE, Kalu UG, Filippini N, Mackay CE, Ebmeier KP. A meta-analysis of diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease [published online ahead of print July 7, 2010].  Neurobiol AgingGoogle Scholar
Hall CB, Lipton RB, Sliwinski M, Katz MJ, Derby CA, Verghese J. Cognitive activities delay onset of memory decline in persons who develop dementia.  Neurology. 2009;73(5):356-36119652139PubMedGoogle ScholarCrossref
Johnson DK, Storandt M, Morris JC, Galvin JE. Longitudinal study of the transition from healthy aging to Alzheimer disease.  Arch Neurol. 2009;66(10):1254-125919822781PubMedGoogle ScholarCrossref