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Logo of neurologyNeurologyAmerican Academy of Neurology
Neurology. 2009 August 25; 73(8): 612–620.
PMCID: PMC2731619

Semantic memory activation in individuals at risk for developing Alzheimer disease

M Seidenberg, PhD, L Guidotti, MS, K A. Nielson, PhD, J L. Woodard, PhD, S Durgerian, BS, P Antuono, MD, Q Zhang, BS, and S M. Rao, PhD



To determine whether whole-brain, event-related fMRI can distinguish healthy older adults with known Alzheimer disease (AD) risk factors (family history, APOE epsilon4) from controls using a semantic memory task involving discrimination of famous from unfamiliar names.


Sixty-nine cognitively asymptomatic adults were divided into 3 groups (n = 23 each) based on AD risk: 1) no family history, no epsilon4 allele (control [CON]); 2) family history, no epsilon4 allele (FH); and 3) family history and epsilon4 allele (FH+epsilon4). Separate hemodynamic response functions were extracted for famous and unfamiliar names using deconvolution analysis (correct trials only).


Cognitively intact older adults with AD risk factors (FH and FH+epsilon4) exhibited greater activation in recognizing famous relative to unfamiliar names than a group without risk factors (CON), especially in the bilateral posterior cingulate/precuneus, bilateral temporoparietal junction, and bilateral prefrontal cortex. The increased activation was more apparent in the FH+epsilon4 than in the FH group. Unlike the 2 at-risk groups, the control group demonstrated greater activation for unfamiliar than familiar names, predominately in the supplementary motor area, bilateral precentral, left inferior frontal, right insula, precuneus, and angular gyrus. These results could not be attributed to differences in demographic variables, cerebral atrophy, episodic memory performance, global cognitive functioning, activities of daily living, or depression.


Results demonstrate that a low-effort, high-accuracy semantic memory activation task is sensitive to Alzheimer disease risk factors in a dose-related manner. This increased activation in at-risk individuals may reflect a compensatory brain response to support task performance in otherwise asymptomatic older adults.


= Alzheimer disease;
= Analysis of Functional NeuroImages;
= analysis of variance;
= area under the curve;
= Brodmann area;
= blood oxygen level–dependent;
= control;
= Dementia Rating Scale 2;
= Diagnostic and Statistical Manual of Mental Disorders, 4th edition;
= episodic memory;
= family history;
= field of view;
= functional region of interest;
= hemodynamic response function;
= mild cognitive impairment;
= Mayo Older Americans Normative Studies;
= magnetic resonance;
= medial temporal lobe;
= not significant;
= Rey Auditory–Verbal Learning Test;
= semantic memory;
= supplementary motor area;
= spoiled gradient-recalled at steady state;
= echo time;
= repetition time;
= voxel-based morphometry.

Two well-established risk factors for the late-onset, sporadic form of Alzheimer disease (AD) are the presence of one or both copies of the apolipoprotein E (APOE) epsilon4 allele and a first-degree family history (FH) of AD.1,2 Task-activated fMRI studies show that cognitively intact older individuals with AD risk factors (FH, APOE epsilon4, or both) exhibit a pattern of increased neural activity compared with individuals without AD risk factors.3 Increased fMRI activity is thought to reflect a compensatory brain response that enables older at-risk individuals to perform at levels equivalent to persons without risk factors.4

Most fMRI studies conducted in preclinical populations (mild cognitive impairment [MCI]) use episodic memory (EM) tasks and focus on medial temporal lobe (MTL) activity. As clinical symptoms emerge, EM performance declines and MTL atrophy increases.5 Not surprisingly, task-activated fMRI findings are inconsistent, with both increased and decreased MTL activation observed.3

We examined the effect of FH and APOE epsilon4 on whole-brain fMRI neural activity in cognitively asymptomatic older adults using a semantic memory (SM) task involving the discrimination of famous from unfamiliar names. This task involves minimal conscious effort and is performed at high accuracy levels. Previously, we demonstrated that cognitively intact older individuals produced increased activity in memory circuits (hippocampus, posterior cingulate, and prefrontal regions) relative to young participants6; however, participants were not separated on AD risk factors. Here, we sought to determine whether a low-effort, high-accuracy SM task would demonstrate differential patterns of activation in at-risk individual using a whole-brain analysis.


Standard protocol approvals, registrations, and patient consents.

This study was approved by the Human Subjects Review Committee of the Medical College of Wisconsin, which oversees the ethical standards of human research being conducted. Written informed consent was obtained from all subjects included in this study.


Healthy adults between ages 65 and 85 years were recruited from newspaper advertisements. A telephone screen, used to determine study eligibility (see below), was administered to 459 individuals. Of the individuals meeting inclusion/exclusion eligibility criteria (see below), 109 agreed to undergo APOE genotype testing from blood samples, neuropsychological evaluation, and an fMRI scanning session. APOE genotype was determined using a PCR method described by Saunders et al.7,8 DNA was isolated with Gentra Systems Autopure LS for Large Sample Nucleic Acid Purification (Minneapolis, MN).

From this pool, 3 subgroups of equal sample size (n = 23), carefully matched on demographic variables (sex, age, and education), were formed based on the presence/absence of at least 1 APOE epsilon4 allele and a family history of dementia. Group 1 (FH+epsilon4) had a family history of dementia and one or both epsilon4 alleles (1 epsilon2/epsilon4; 21 epsilon3/epsilon4; 1 epsilon4/epsilon4). Group 2 (FH) had a family history of dementia but did not possess an APOE epsilon4 allele (6 epsilon2/epsilon3; 17 epsilon3/epsilon3). Group 3 (controls [CON]) consisted of individuals who reported no family history of dementia and did not possess an APOE epsilon4 allele (1 epsilon2/epsilon3; 22 epsilon3/epsilon3). Equal sample sizes were required to avoid biases in the image analyses. As expected, no significant group differences were observed on demographic variables of age, sex, or education (table 1).

Table thumbnail
Table 1 Group demographics, neurobehavioral testing, and fMRI task performance

Family history was defined as a report of a clear clinical diagnosis of AD in first-degree relatives—parents or siblings (probable AD; 63.2%)—or a reported history of gradual decline in memory and other cognitive functions, confusion, or judgment problems without a formal diagnosis of AD before death. One participant reported a diagnosis of AD in a second-degree relative, with some mild cognitive changes noted in a parent before the parent’s death.

Participants were excluded if they reported a history of neurologic disease, medical illnesses, major psychiatric disturbance meeting DSM-IV Axis I criteria, a Geriatric Depression Scale score greater than 10, substance abuse meeting DSM-IV Axis I criteria, or were currently taking psychoactive medications. Participants were allowed to take cardiovascular drugs. No between-group differences were observed in the percent of participants taking blood pressure medications; FH+epsilon4 participants were more likely to be taking statins to lower cholesterol levels than were the FH and CON participants (59% vs 25%; χ2 = 4.7, p < 0.3). A blood chemistry screen (thyroid-stimulating hormone, homocysteine, vitamin B12, folate, and creatinine) was not found to be clinically significant in any of the participants. Additional exclusion criteria related to fMRI scanning included pregnancy, weight inappropriate for height, ferrous objects within the body, low visual acuity, and a history of claustrophobia. Only right-handed participants were included based on the Edinburgh Handedness Inventory.9


Neuropsychological testing and the fMRI scanning were conducted on the same day. Participants were asked to refrain from alcohol use for 24 hours and caffeine use for 12 hours before testing. The neuropsychological test battery consisted of the Mini-Mental State Examination,10 Mattis Dementia Rating Scale 2,11 Rey Auditory Verbal Learning Test,12 Geriatric Depression Scale,13 and Lawton Activities of Daily Living.14 All participants received financial compensation.

Functional MRI.

The task stimuli consisted of 30 names of famous persons and 30 names of unfamiliar individuals selected from an original pool of 784 names because of a high rate of identification (>90% correct).15 A trial consisted of the visual presentation of a single name for 4 seconds. Participants were instructed to make a right index finger key press if the name was famous and a right middle finger key press if the name was unfamiliar. Both accuracy (percentage correct) and reaction time (in milliseconds) were recorded; signal detection indexes (A′ and B′′) were calculated to examine discriminability and response bias.16,17 The 60 name trials were randomly interspersed with thirty 4-second trials in which the participant was instructed to fixate on a single centrally placed crosshair. This was done to introduce “jitter” into the fMRI time course. The imaging run began and ended with 12 seconds of fixation. The total time for the single imaging run was 5 minutes 24 seconds.

fMRI acquisition.

Whole-brain, event-related fMRI was conducted on a General Electric (Waukesha, WI) Signa Excite 3.0-T short bore scanner equipped with a quad split quadrature transmit/receive head coil. Echoplanar images were collected using an echoplanar pulse sequence (echo time [TE] = 25 msec; flip angle = 77 degrees; field of view [FOV] = 24 mm; matrix size = 64 × 64). Thirty-six contiguous axial 4-mm-thick slices were selected to provide coverage of the entire brain (voxel size = 3.75 × 3.75 × 4 mm). The interscan interval (TR) was 2 seconds. High-resolution, 3-dimensional spoiled gradient-recalled at steady state (SPGR) anatomic images were acquired (TE = 3.9 msec; repetition time [TR] = 9.5 msec; inversion recovery preparation time = 450 msec; flip angle = 12 degrees; number of excitations = 2; slice thickness = 1.0 mm; FOV = 24 cm; resolution = 256 × 224). Foam padding was used to reduce head movement within the coil.

Image analysis.

Functional images were generated with the Analysis of Functional NeuroImages (AFNI) software package.18 Each image time series was time shifted to the beginning of the TR and then spatially registered to reduce the effects of head motion using a rigid body iterative linear least squares method. A deconvolution analysis was used to extract a hemodynamic response function (HRF) for famous and unfamiliar names from the time series. HRFs were modeled for the 0- to 16-second period poststimulus onset. Motion parameters were incorporated into the model as nuisance regressors. The HRFs were also transposed so that the value of the HRF at trial onset was zero. Despite the high task accuracy rate (see below), estimation of the HRFs for identification of famous names and rejection of unfamiliar names was restricted to correct trials. Area under the curve (AUC) was calculated by summing the hemodynamic responses at time points 4, 6, and 8 seconds after trial onset. Individual anatomic and functional scans were transformed into standard stereotaxic space.19 To compensate for normal variation in anatomy across subjects, functional images were blurred using a 6-mm gaussian full-width half-maximum filter.

Spatial extent analysis.

This analysis was performed to examine within-group differences in the spatial extent of activation comparing the famous and unfamiliar name conditions. For each group, statistical parametric maps were generated to identify voxels where the AUC for famous names differed significantly from the AUC for unfamiliar names. An individual voxel probability threshold [t(22) = 3.12, p = 0.005] was coupled with a minimum cluster volume threshold of 0.731 mL. This combination of individual voxel probability and minimum cluster size thresholds is equivalent to a whole brain family-wise error threshold of p < 0.05 based on 3,000 Monte Carlo simulations.20

Functional region of interest group analysis.

As a follow-up to the voxel-wise analyses, a functional region of interest (fROI) analysis was conducted to evaluate potential group differences in the magnitude of the AUC in functionally active regions. An fROI map was generated by conjoining activated regions identified in the spatial extent analysis (see above) across the 3 groups. Any voxel deemed “activated” by the famous–unfamiliar name subtraction in at least 1 of the 3 groups contributed to the final fROI map. For each participant, an “averaged HRF” was calculated for all voxels within an fROI. AUC (4, 6, and 8 seconds after stimulus onset) served as the dependent variable in a 1-way analysis of variance (ANOVA) to examine group differences in each fROI.

Voxel-based morphometry.

Voxel-based morphometry (VBM) was conducted using SPGR anatomic images segmented with SPM 5.21,22 A cutoff gray matter probability (p = 0.01) was used to remove spurious signals at gray matter–white matter boundaries. After transforming anatomic images into Montreal Neurological Institute coordinates using a standard template, a study-specific template was created to normalize subjects into a common stereotaxic space. Modulated, normalized gray matter images were blurred using a 12-mm gaussian filter to compensate for normal variation in anatomy across subjects. A voxel-wise, 1-way ANOVA (unpooled variance across subjects) was used to examine differences in cortical atrophy across the 3 participant groups, using a family-wise error threshold of p < 0.05.


Neuropsychological and fMRI task performance.

No significant group differences were observed on neuropsychological tests of global cognition, verbal learning, depression, and activities of daily living (table 1). Likewise, no significant group differences were observed on accuracy, discriminability (A′), response bias (B′′), or reaction time for the fMRI task (table 1). Mean accuracy on the fame discrimination task exceeded 90% correct for all 3 groups. The relatively low effect sizes (η2) suggest that the nonsignificant findings were not influenced by sample size.

Voxel-based morphometry.

A 1-way ANOVA identified no brain regions demonstrating significant group differences in gray matter density, a measure of cortical atrophy.

fMRI–spatial extent analysis.

Voxels demonstrating significant differences in the AUC for the famous and unfamiliar name stimuli are shown in figure 1 and table 2 for each of the 3 groups (CON, FH, FH+epsilon4). Both risk groups demonstrated regions with increased magnetic resonance (MR) signal for famous vs unfamiliar names; no regions demonstrated greater activity for unfamiliar than for famous names. In contrast, the CON group showed predominantly increased signal for unfamiliar relative to famous names. Total volume of activation (famous > unfamiliar comparison) was largest in the FH+epsilon4 group (35.7 mL), intermediate in the FH group (22.4 mL), and smallest in the CON group (6.3 mL). In general, the FH+epsilon4 group had a larger spatial extent of activation in the same regions activated by the FH group (e.g., posterior cingulate/precuneus, left and right middle temporal gyrus, left inferior parietal cortex; table 2), but also recruited additional areas (e.g., right middle frontal gyrus, right inferior parietal lobule).

figure znl0320968410001
Figure 1 Results of voxel-wise analysis demonstrating significant differences between the famous and unfamiliar name conditions
Table thumbnail
Table 2 Activation foci for famous vs unfamiliar name subtraction

fMRI–fROI analysis.

A conjunction analysis (see above) identified 21 fROIs (figure 2 and table 3). Eleven regions showed greater blood oxygen level–dependent (BOLD) activity for famous compared with unfamiliar names, and 10 regions showed the opposite pattern (the latter derived entirely from the CON group). Figure e-1 on the Neurology® Web site at shows the averaged famous and unfamiliar HRFs for each participant group for 8 representative brain regions, as well as the HRF derived by subtracting the unfamiliar from famous conditions. Representative AUC estimates derived from the famous–unfamiliar subtraction are presented in the rightmost column of figure e-1.

figure znl0320968410002
Figure 2 Brain regions used in fROI analysis
Table thumbnail
Table 3 Group differences derived from functional region of interest analysis

A 1-way repeated-measures ANOVA performed on the AUC estimates indicated significant group differences in 16 of 21 regions (table 3). Seven of the 11 fROIs exhibiting greater activity for famous than for unfamiliar names demonstrated significant overall group differences, which were then subjected to a post hoc pairwise group comparison. In 4 regions, bilateral precuneus/posterior cingulate, bilateral medial frontal, left angular gyrus, and right middle temporal, the FH+epsilon4 and FH groups had significantly greater MR signal intensity than the CON group. In one region, right middle frontal, the FH+epsilon4 group had significantly greater activity than the FH and CON groups. In another region, right inferior parietal/supramarginal gyrus, the FH+epsilon4 group had significantly greater activity than the FH, which in turn exhibited greater activity than the CON group. Finally, in the bilateral caudate, the CON group had greater activity than the 2 risk groups (FH+epsilon4 and FH).

Nine of the 10 fROIs exhibiting greater activity for unfamiliar than for famous names demonstrated significant group differences. In 8 of the 10 regions (bilateral supplementary motor area [SMA], left and right precentral, left inferior frontal, right precuneus/angular gyrus, left postcentral, right inferior occipital, left middle occipital), the CON group demonstrated greater activity in response to the unfamiliar relative to famous name stimuli; the 2 risk groups (FH+epsilon4 and FH), in contrast, demonstrated equivalent degrees of activation for the 2 types of stimuli within these regions (figure e-1B). In one region, the right insula, this pattern was only observed between the FH+epsilon4 and CON group.

In contrast, controls showed a consistent pattern, seen in 8 of 10 regions, for increased activity for unfamiliar names compared with famous names. These included bilateral SMA, left precentral and right precentral gyrus, right precuneus, left inferior frontal, left postcentral gyrus, and left inferior occipital gyrus and right insula. The HDR and AUC for several of the regions showing these different patterns are shown separately for the famous, unfamiliar, and famous–unfamiliar contrast in figure e-1.


Our results indicate that AD risk factors exert a strong influence on patterns of brain activation observed in cognitively intact older individuals. In response to a low-effort, high-accuracy SM task, the FH and FH+epsilon4 groups demonstrated greater activation in response to famous relative to unfamiliar names, predominantly in the bilateral posterior cingulate/precuneus, bilateral temporoparietal junction, and bilateral prefrontal cortex. Furthermore, the spatial extent of activated tissue was greater in the FH+epsilon4 (35.7 mL) than in the FH (22.4 mL) group. Similarly, the magnitude of the fMRI response was significantly greater in the FH+epsilon4 than in the FH group in 2 right hemisphere regions (middle frontal and supramarginal gyri). In contrast, the group without risk factors evidenced greater activation in response to unfamiliar than in response to famous stimuli. This pattern of group differences in brain activation could not be explained by demographic variables, cerebral atrophy, verbal EM performance, global cognitive functioning, activities of daily living, or depression.

These findings are generally consistent with the functional recruitment hypothesis of age-related compensatory changes in the fMRI activation patterns of at-risk populations.5,6,23–26 Previous studies, however, used effortful EM activation tasks. Our study extends the previous work by showing that AD risk factors can exert influence on brain activation patterns even when participants engage in a low-effort and relatively automatic SM task. These findings have practical significance for tracking changes in brain activation longitudinally, because performance on similar SM tasks remains high in MCI and early AD patients.

An additional methodologic advantage of this study was the use of an event-related trial design. Most fMRI studies of at-risk populations have used blocked trial designs, which are not able to eliminate incorrect trials from the HRF estimation. Event-related designs enable removal of incorrect trials from the resulting activation maps. We suspect that one source of inconsistency in the fMRI literature, especially in clinically symptomatic groups (MCI, early AD), results from this confound.

Most studies to date have focused primarily on activation observed within the hippocampus and adjacent MTL regions on EM tasks. This study, as well as others by our group,6,26 clearly demonstrates that functional recruitment occurs in extrahippocampal regions (posterior cingulate, lateral posterior temporoparietal) in cognitively intact at-risk individuals. Focusing on these neocortical memory circuits mitigates the problems of measuring brain activity solely in the MTL, one of the first regions to demonstrate atrophy in MCI and early AD.27

Our findings also suggest that having multiple risk factors (FH and epsilon4) may exert a stronger influence on brain activation patterns than having a single factor (FH). This effect has been observed in previous studies,28–30 although the direction of the altered pattern of activation for the combined risk group has not been consistent. Several methodologic variations between studies might account for the divergent findings.31

Controls showed a greater fMRI response for unfamiliar compared with famous names, a pattern opposite to that seen in the 2 at-risk groups. These regions, including the SMA, left and right precentral gyri, left inferior frontal gyrus, right insula, precuneus, and angular gyrus, are frequently activated by language, attentional, and working memory circuits. We speculate that the CON group allocated more resources to rule out unfamiliar names than to identify famous names.

We did not adopt a calibration approach, such as CO2/O2 inhalation or a hypercapnic challenge (breath holding),32 to scale the BOLD response to reduce intersubject variability, a possible study limitation. In addition, we note that a significantly higher percentage of FH+epsilon4 subjects were taking statins, suggesting possible cardiovascular group differences that could conceivably influence the BOLD response.

Longitudinal fMRI studies are required to determine whether the differential pattern of SM activation observed in this cross-sectional study predicts future cognitive decline and enables the precise tracking of the clinical course during the preclinical phase of AD.


Ms. Durgerian receives salary support from the NIH [NIA R01-AG022304] and Advancing Healthier Wisconsin Foundation; her husband holds patents unrelated to the work described in this publication: US Patent No. 7,244,572 issued on 07/17/2007 and US Patent No. 5115000 issued on 05/19/1992. Dr. Seidenberg received funding support from the NIH [NIA RO1-AG022304 (Coinvestigator) and NINCDS RO1-NS44351 (Coinvestigator)]. He serves as a consulting editor for the Journal of Neuropsychology. Dr. Seidenberg receives royalties from a self-report questionnaire that he developed. Ms. Guidotti received a travel award from Rosalind Franklin University. Dr. Nielson serves as a consulting editor for the Journal of Neuropsychology; receives research support from the NIH [NIA R01-AG022304 (Coinvestigator and Site PI) and NIOSH T 42 OH008672 (Coinvestigator)], the Medical College of Wisconsin, Marquette University, and the Wisconsin Women’s Health Foundation; and has served as an expert witness (consultation only) for Dorsey, Dorsey & Whitney LLP, Minneapolis, MN. Dr. Woodard serves as a consulting editor for the Journal of Athletic Training, Neuropsychology, and Aging, Neuropsychology, and Cognition; has received consultant fees from the University of Oklahoma and the National Rehabilitation Hospital; serves/has served as a neuropsychology consultant for the Atlanta Thrashers Hockey Club, the Chicago Wolves Hockey Club, and the Atlanta Falcons Football Club; and receives research support from the NIH [NIA P01-AG17553 (Program Project Coinvestigator and Co-Project Leader), NIA R01-AG022304 (Coinvestigator), NIMH R21-MH069704 (Co-PI)]. Dr. Antuono serves on the speakers’ bureaus of Novartis and Pfizer; receives research support from Elan Pharmaceuticals, Eisai, GlaxoSmithKline, the Helen Bader Foundation, and the Advancing Healthier Wisconsin Foundation. Ms. Zhang received salary support from the NIH [NIA R01-AG022304]. Dr. Rao served as founder and chief science officer of Neurognostics, Inc.; serves/has served as Editor of Neuropsychology, Associate Editor of the Journal of the International Neuropsychological Society, and a member of the editorial board of Brain and Cognition, Brain Imaging and Behavior, Journal of Clinical and Experimental Neuropsychology, Journal of the International Neuropsychological Society, Journal of Neurological Rehabilitation, Neuropsychology, Neuropsychology Review, and NMR in Biomedicine; received royalties from publication of Neuropsychiatry (Wolters Kluwer Health, Inc., 2005–2008) and a neuropsychological test (PASAT); received honorarium for serving as journal editor from the American Psychological Association; has served as consultant or on the speakers’ bureaus for Biogen Idec, Genentech, Pfizer, EMD Serono, Brintnall & Nicolini, Inc., and the Nationwide Children’s Hospital; and has received research support from the NIH [NIA R01-AG022304 (PI) and NINDS R01-NS054893 (Co-PI)], US Department of Defense [PTSD/TBI Research Program W81XWH-08-2-0124 (PI)], CHDI Foundation (PI), Advancing Healthier Wisconsin Foundation (PI), Biogen Idec (PI), and EMD Serono (PI).

Supplementary Material

[Data Supplement]


Address correspondence and reprint requests to Dr. Stephen M. Rao, Schey Center for Cognitive Neuroimaging, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave./U10, Cleveland, OH 44195 gro.fcc@2soar

Supplemental data at

Supported in part by grants from the NIH (R01-AG022304, M01-RR00058) and Advancing Healthier Wisconsin Foundation.

Disclosure: Author disclosures are provided at the end of the article.

Received January 13, 2009. Accepted in final form May 18, 2009.


1. Corder EH, Saunders AM, Strittmatter WJ, et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 1993;261:921–923. [PubMed]
2. Devi G, Ottman R, Tang MX, Marder K, Stern Y, Mayeux R. Familial aggregation of Alzheimer disease among whites, African Americans, and Caribbean Hispanics in northern Manhattan. Arch Neurol 2000;57:72–77. [PubMed]
3. Wierenga CE, Bondi MW. Use of functional magnetic resonance imaging in the early identification of Alzheimer’s disease. Neuropsychol Rev 2007;17:127–143. [PMC free article] [PubMed]
4. Bookheimer SY, Strojwas MH, Cohen MS, et al. Patterns of brain activation in people at risk for Alzheimer’s Disease. N Engl J Med 2000;343:450–456. [PMC free article] [PubMed]
5. Twamley EW, Ropacki SA, Bondi MW. Neuropsychological and neuroimaging changes in preclinical Alzheimer’s disease. J Int Neuropsychol Soc 2006;12:707–735. [PMC free article] [PubMed]
6. Nielson KA, Douville KL, Seidenberg M, et al. Age-related functional recruitment for famous name recognition: an event-related fMRI study. Neurobiol Aging 2006;27:1494–1504. [PMC free article] [PubMed]
7. Mayeux R, Saunders AM, Shea S, et al. Utility of the apolipoprotein E genotype in the diagnosis of Alzheimer’s disease. Alzheimer’s Disease Centers Consortium on Apolipoprotein E and Alzheimer’s Disease. N Engl J Med 1998;338:506–511. [PubMed]
8. Saunders AM, Hulette O, Welsh-Bohmer KA, et al. Specificity, sensitivity, and predictive value of apolipoprotein-E genotyping for sporadic Alzheimer’s disease. Lancet 1996;348:90–93. [PubMed]
9. Oldfield RC. The assessment of handedness: the Edinburgh Inventory. Neuropsychologia 1971;9:97–111. [PubMed]
10. Folstein MF, Folstein SE, McHugh PR. “Mini-Mental State”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189–198. [PubMed]
11. Mattis S. Dementia Rating Scale Professional Manual. Odessa, FL: Psychological Assessment Resources; 1988.
12. Rey A. L’examen clinique en psychologie. Paris: Presses Universitaires de France; 1958.
13. Yesavage JA, Brink TL, Rose TL, et al. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res 1982;17:37–49. [PubMed]
14. Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 1969;9:179–186. [PubMed]
15. Douville K, Woodard JL, Seidenberg M, et al. Medial temporal lobe activity for recognition of recent and remote famous names: an event-related fMRI study. Neuropsychologia 2005;43:693–703. [PubMed]
16. Aaronson D, Watts B. Extensions of Grier’s computational formulas for A′ and B′′ to below-chance performance. Psychol Bull 1987;102:439–442. [PubMed]
17. Grier JB. Nonparametric indexes for sensitivity and bias: computing formulas. Psychol Bull 1971;75:424–429. [PubMed]
18. Cox R. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 1996;29:162–173. [PubMed]
19. Talairach J, Tournoux P. A Co-planar Stereotaxic Atlas of a Human Brain. Stuttgart: Thieme; 1988.
20. Ward BD. Simultaneous inference for fMRI data (June 2000). Available at: Accessed June 1, 2009.
21. Ashburner J, Friston KJ. Unified segmentation. Neuroimage 2005;26:839–851. [PubMed]
22. Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 2001;14:21–36. [PubMed]
23. Buckner RL. Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron 2004;44:195–208. [PubMed]
24. Grady CL, McIntosh AR, Beig S, Keightley ML, Burian H, Black SE. Evidence from functional neuroimaging of a compensatory prefrontal network in Alzheimer’s disease. J Neurosci 2003;23:986–993. [PubMed]
25. Nielson KA, Langenecker SA, Garavan H. Differences in the functional neuroanatomy of inhibitory control across the adult life span. Psychol Aging 2002;17:56–71. [PubMed]
26. Woodard JL, Seidenberg M, Nielson KA, et al. Temporally graded activation of neocortical regions in response to memories of different ages. J Cogn Neurosci 2007;19:1113–1124. [PMC free article] [PubMed]
27. Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 1991;82:239–259. [PubMed]
28. Lind J, Persson J, Ingvar M, et al. Reduced functional brain activity response in cognitively intact apolipoprotein E epsilon4 carriers. Brain 2006;129:1240–1248. [PubMed]
29. Fleisher AS, Houston WS, Eyler LT, et al. Identification of Alzheimer disease risk by functional magnetic resonance imaging. Arch Neurol 2005;62:1881–1888. [PubMed]
30. Smith CD, Andersen AH, Kryscio RJ, et al. Altered brain activation in cognitively intact individuals at high risk for Alzheimer’s disease. Neurology 1999;53:1391–1396. [PubMed]
31. Trivedi MA, Schmitz TW, Ries ML, et al. Reduced hippocampal activation during episodic encoding in middle-aged individuals at genetic risk of Alzheimer’s disease: a cross-sectional study. BMC Med 2006;4:1. [PMC free article] [PubMed]
32. Thomason ME, Foland LC, Glover GH. Calibration of BOLD fMRI using breath holding reduces group variance during a cognitive task. Hum Brain Mapp 2007;28:59–68. [PubMed]

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