Search tips
Search criteria 


Logo of neurologyNeurologyAmerican Academy of Neurology
Neurology. 2011 August 2; 77(5): 469–475.
PMCID: PMC3146305

Event-related potential markers of brain changes in preclinical familial Alzheimer disease



Event-related potentials (ERPs) can reflect differences in brain electrophysiology underlying cognitive functions in brain disorders such as dementia and mild cognitive impairment. To identify individuals at risk for Alzheimer disease (AD) we used high-density ERPs to examine brain physiology in young presymptomatic individuals (average age 34.2 years) who carry the E280A mutation in the presenilin-1 (PSEN1) gene and will go on to develop AD around the age of 45.


Twenty-one subjects from a Colombian population with familial AD participated: 10 presymptomatic subjects positive for the PSEN1 mutation (carriers) and 11 siblings without the mutation (controls). Subjects performed a visual recognition memory test while 128-channel ERPs were recorded.


Despite identical behavioral performance, PSEN1 mutation carriers showed less positivity in frontal regions and more positivity in occipital regions, compared to controls. These differences were more pronounced during the 200–300 msec period. Discriminant analysis at this time interval showed promising sensitivity (72.7%) and specificity (81.8%) of the ERP measures to predict the presence of AD pathology.


Presymptomatic PSEN1 mutation carriers show changes in brain physiology that can be detected by high-density ERPs. The relative differences observed showing greater frontal positivity in controls and greater occipital positivity in carriers indicates that control subjects may use frontally mediated processes to distinguish between studied and unstudied visual items, whereas carriers appear to rely more upon perceptual details of the items to distinguish between them. These findings also demonstrate the potential usefulness of ERP brain correlates as preclinical markers of AD.

Recognition memory impairments in Alzheimer disease (AD) have been linked to neocortical association areas including temporal and parietal lobes.1 Event-related potentials (ERPs) are less expensive, more widely available, and more comfortable than many other imaging modalities (e.g., MRI, PET, SPECT). ERPs, along with other EEG measures, have proven to be a useful marker in neurodegenerative conditions.25 ERP components of recognition memory are sensitive to decline in old age6 and amnestic mild cognitive impairment (aMCI).7 Studies have proposed ERPs as a sensitive method for early detection of AD, separating EEG activity related to AD pathology from normal aging.812 Preclinical markers and early detection are increasingly important as research on new treatments that may slow or halt decline in AD are under development.13,14

Familial AD (FAD) allows the study of presymptomatic stages of AD that may be relevant for sporadic AD. Presenilin-1 (PSEN1) mutation carriers develop neuropathologic changes in cortical association areas and subcortical systems,15 signs and symptoms that can be indistinguishable from those with sporadic AD, with a mean age of 45 at clinical onset.1619 Studies in FAD have demonstrated preclinical changes in morphometry,20,21 regional brain activation,2224 functional connectivity,25 and ERPs.8,9 ERP preclinical changes have been shown in auditory stimulus discrimination8 and semantic processing.9 ERPs of recognition memory have not yet been evaluated in FAD.

Using an ERP picture paradigm proven sensitive to changes in recognition memory in older adults6 and aMCI,7 we examined young cognitively intact individuals who carry a PSEN1 mutation causative of FAD.



A total of 21 young participants were recruited from the Familial Colombian AD population studied at the University of Antioquia, Medellin, Colombia; 10 participants were carriers of the E280A PSEN1 mutation and 11 were PSEN1 mutation negative and served as controls. Participants had a minimum of 9 years of education. Groups were matched for age, sex, education, and neuropsychological assessment performance (table 1). Neuropsychological assessment consisted of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) battery, which has been adapted to this Colombian population.26 No participants had cognitive impairment as reported by their most recent neuropsychological assessment, which was done within 6 months prior to the time of the ERP session. Researchers were blind to the genetic status of the participants during data collection.

Table 1
Subject demographic information and CERAD neuropsychological test batterya

Standard protocol approvals, registrations, and patient consents.

The study was approved by both the institutional review board committees of the University of Antioquia and Boston University. All subjects gave signed informed consent before participating.

Experimental materials and methods.

Participants performed a recognition memory task using color pictures of concrete and namable objects: 50 new stimuli were presented during the study phase, and 100 stimuli (50% old) were presented during the test phase. The pictures used in the study were obtained from a stimuli set previously used by Ally et al.7 and Ally and Budson.27 Pictures were counterbalanced across study-test lists. In addition, test conditions (old, new) were counterbalanced across subjects. Color pictures were presented in central vision on a white background, with an average height of 13 cm and an average width of 15 cm, and a visual angle subtended of 7 degrees. All stimuli were presented on a 17-inch flat screen computer monitor positioned 48 inches from the subject. Each trial began with a 1,000-msec fixation character (“+”) prior to the presentation of the stimuli. Study stimuli were then presented for 2,000 msec followed by the question, “Do you like this item?” Subjects were then prompted to button press to signify their like/dislike judgment and to remember the items for a subsequent memory test. Test stimuli were presented for 1,500 msec, followed by the question, “Is this item old or new?” Subjects were then prompted to button press to signify their old/new judgment. Subjects were asked to hold their responses until the question appeared immediately after stimuli presentation to minimize response-related ERP artifact. We acknowledge that asking participants to keep their response “in mind” (or alternatively, inhibiting their natural inclination to respond before the prompt) may affect the electrophysiologic data, particularly the late components. However, because subjects would be engaging in this activity in all trials, this activity should be removed when subtracting correct rejections from hits.

ERP procedure.

Subjects were seated in a hardback chair and fitted with an Active Two-electrode cap (Behavioral Brain Sciences Center, Birmingham, UK). A full array of 128 Ag–AgCl BioSemi (Amsterdam, the Netherlands) “active” electrodes were connected to the cap in a preconfigured montage, which places each electrode in equidistant concentric circles from 10–20 position, Cz. In addition to the 128 scalp electrodes, mini-biopotential electrodes were placed on each mastoid process. Finally, vertical and horizontal electro-oculography (EOG) activity was recorded from bipolar electrodes placed below the left eye and on the outer canthus of the left and right eye. EEG and EOG activity were amplified with a bandwidth of 0.03–35Hz (3 dB points) and digitized at a sampling rate of 256 Hz. Recordings were referenced to a vertex reference point, but were later re-referenced to a common average reference to minimize the effects of reference site activity and accurately estimate the scalp topography of the measured electrical fields.27 The sampling epoch for each test trial lasted for a total of 1,000 msec, which included a 200-msec prestimulus baseline period. This prestimulus period was used to baseline correct averaged ERP epochs lasting 800 msec. ERPs were averaged and corrected using the EMSE Software Suite (Source Signal Imaging, San Diego, CA). Trials were corrected for excessive EOG activity using the EMSE Ocular Artifact Correction Tool. The tool first allows the investigator to manually distinguish artifact data from artifact-free data. Then, using a covariance technique that simultaneously models artifact and artifact-free EEG, a logarithmic ratio of artifact data to clean data is produced by EMSE. Finally, ocular artifact is subtracted from the recording where it is detected by the correction tool. Trials were discarded from the analyses if they contained baseline drift or movement greater than 90 V. Individual bad channels were corrected with the EMSE spatial interpolation filter.

Behavioral analysis.

Recognition accuracy was calculated using the discrimination index Pr (% hits − % false alarms) to compare the performance of the PSEN1 mutation carriers and the controls. The discrimination values were submitted to a factorial analysis of variance (ANOVA) using group as between-subject factor.

ERP analysis.

We performed 2 sets of analyses on the ERP data. For the first analysis, mean amplitudes were calculated for time intervals of every 100 msec from 0 msec to 800 msec (after stimulus presentation), which were then averaged across groups of 7 electrodes that formed 10 separate regions of interest (ROI) (central anterior inferior, left anterior inferior [LAI], right anterior inferior, left anterior superior, right anterior superior, left posterior superior [LPS], central posterior superior, right posterior superior, left posterior inferior, and right posterior inferior). An omnibus mixed-factor ANOVA was performed using the factors of group (PSEN1 carriers and controls), item type (hits and correct rejections), time interval (0–100 msec, 100–200 msec, 200–300 msec, 300–400 msec, 400–500 msec, 500–600 msec, 600–700 msec, and 700–800 msec), and ROI (the 10 ROIs). Follow-up ANOVAs were performed as appropriate within time intervals and included the factors of group, item type, and ROI. Statistical analyses were performed using statistical software (SPSS version 16.0; SPSS Inc., Chicago, IL).

For the second analysis, we performed nonparametric permutation tests on the old/new scalp topographies for both groups. These permutation tests calculate the statistical probability of differences between groups or conditions in p values at every electrode for every millisecond without averaging across time.

The waveforms and scalp topographies were formed by averaging a series of trials for each subject; the mean number of trials for PSEN1 carriers (36 hits and 36 correct rejections) and control subjects (38 hits and 36 correct rejections) was similar. All topographic maps represent an average of 100 msec going forward from the labeled time (e.g., “0 msec” represents the average from 0 to 99 msec).

Stepwise discriminant analysis.

Stepwise discriminant analysis was used on the time intervals in which there was a statistically significant interaction between group, item type, and ROI to quantify the ability of ERP measures to successfully classify individuals according to the FAD-related mutation.


Behavioral performance.

Both groups performed near ceiling in terms of recognition memory discrimination (controls: 0.92, SD 0.03; PSEN1 carriers: 0.92, SD 0.03). There was no significant difference in median reaction time between the controls (655.29 msec, SD 136.8) and PSEN1 carriers (629.40 msec, SD 127.3) (F1,19 = 0.39, p = 0.53).

ERP results.


The initial omnibus mixed-factor ANOVA revealed significant interactions of item type, ROI, time interval, and group (F1,63 = 1.71, p = 0.001), ROI and time interval (F1,63 = 7.23, p = 0.001), item type and ROI (F1,9 = 9.30, p = 0.001), item type, ROI, and time interval (F1,63 = 4.42, p = 0.001). In order to understand the 4-way and other interactions, separate ANOVAs for each time interval were performed. Only the omnibus mixed-factor ANOVAs for the 200 to 300 msec interval revealed a significant interaction of item type, ROI, and group (F1,9 = 4.06, p = 0.01). Post hoc independent sample t tests for hits and correct rejections between groups at the 200–300 msec time interval revealed that hits at ROI left posterior superior (t [19] = 2.04, p = 0.05) were significantly less positive for PSEN1 mutation carriers compared to controls. Correct rejections were significantly different between groups at ROI right anterior superior (t [19] = −2.40, p = 0.02), ROI left posterior superior (t [19] = 2.47, p = 0.02), and ROI left posterior inferior (t [19] = 2.10, p = 0.04). In this case, correct rejections were more positive for PSEN1 carriers at ROI right anterior superior and more positive for controls at ROI left posterior superior and ROI left posterior inferior. Paired sample t tests for hits vs correct rejections in PSEN1 carriers alone showed that hits were more positive than correct rejections at ROIs right posterior superior (t [9] = 2.67, p = 0.02) and ROI right posterior inferior (t [9] = 2.74, p = 0.02), and more negative at ROI left anterior inferior (t [9] = −2.79, p = 0.02). A similar analysis in controls did not show any statistically significant differences.

ANOVAs at other time intervals (100–200 msec, 300–400 msec, 400–500 msec, 500–600 msec, 600–700 msec, 700–800 msec) revealed significant interactions between item type and ROI, but not group (table 2).

Table 2
Significant effect and interactions from ANOVAs at every 100-ms interval from 0 to 800 ms

Grand average hit and correct rejection ERP waveforms for PSEN1 mutation carriers and controls can be seen in figure 1.

Figure 1

An external file that holds a picture, illustration, etc.
Object name is znl0291190300001.jpg
PSEN1 carriers and controls grand average hit and correct rejection event-related potential (ERP) waveforms

Each waveform represents the composite average of the 7 electrodes subsuming 10 different regions of interest (ROI). ROIs are listed to the left of each waveform: central anterior inferior (CAI), left anterior inferior (LAI), right anterior inferior (RAI), left anterior superior (LAS), right anterior superior (RAS), left posterior superior (LPS), central posterior superior (CPS), right posterior superior (RPS), left posterior inferior (LPI), and right posterior inferior (RPI).

Nonparametric analyses.

Scalp topography maps showed the expected old/new effect at right superior and inferior right frontal regions between 300 and 500 msec in both groups (figure 2A). Between-group nonparametric analyses revealed that the old/new effect was greater at bilateral frontal electrodes, with lesser extent in the left frontal regions for the PSEN1 carriers compared to the controls. These frontal differences began early in the recording interval (~200 msec) and continued uninterrupted throughout most of the recording. The nonparametric analyses also revealed that the left frontal regions were less positive for the PSEN1 carriers than for the controls, whereas a small area in the center posterior region was more positive for the PSEN1 carriers than for controls from 650 to 800 msec. Right posterior regions were more positive for the PSEN1 carriers compared to controls throughout most of the recording epoch, especially evident at early time intervals.

Figure 2

An external file that holds a picture, illustration, etc.
Object name is znl0291190300002.jpg
PSEN1 carriers and controls old/new scalp topography maps

Topographies are presented in 100 msec averages going forward. (A) Averaged old/new scalp topographies for each group of subjects (controls and PSEN1 carriers). (B) Old/new scalp topographies for 2 typical subjects from each group. (C) Results of the between-group nonparametric analysis showing the early event-related potential differences in the posterior regions.

Topographic scalp distributions for representative individuals are shown in figure 2B. The early posterior differences evidenced by the nonparametric analyses during the time window 200–300 msec were observed at the individual level in 7 out 10 of the PSEN1 carriers, but only in 3 out of 11 controls.

Stepwise discriminant analysis.

To directly examine the predictive potential of the ERP measures, a stepwise discriminant analysis was also performed at the 200–300 msec interval with the 10 ROIs for hits and correct rejections. Prediction of a given subject's classification was based upon a model that did not include that subject. The output model included correct rejections at ROI LPS and hits at ROI LAI. A total of 81.8% (9/11) of control subjects and 72.7% (8/10) of PSEN1 carriers were correctly classified (χ2 = 11.194, df = 2, p = 0.004).


The present study found evidence to suggest that subtle differences in the neural processes associated with visual recognition memory occur very early in carriers of the PSEN1 mutation, years before the onset of cognitive symptoms and the development of AD. While both groups evoked the characteristic ERP pattern during recognition memory, control subjects exhibited activation patterns reliably associated with frontally mediated processes that distinguish between studied and unstudied visual items,27 while carriers exhibited more brain activity in occipital regions that have been associated with visual perceptual processing.28 Increases of occipital activity have been reported previously in an ERP study of word recognition memory in patients with aMCI,7 and in a PET study of successful verbal recognition in patients with mild AD.29 AD is thought to cause a functional decline associated with posterior cortical dysfunction,30 and a variety of visual disorders including impairments of contrast sensitivity, motion perception, and navigation have been associated with memory problems observed in AD.28 The pattern of posterior activity observed in our PSEN1 carriers may reflect an early AD-related synaptic dysfunction or a neural compensation process that requires that carriers recruit more the posterior regions during recognition memory in order to perform equally well as controls. These 2 processes may be impacting the way that their brains recognize items previously learned, and which may occur decades prior to recognizable cognitive symptoms. This would suggest that young presymptomatic PSEN1 carriers rely more on bottom-up perceptual factors or physical features of the items to make recognition memory decisions, which in turn may help to maintain their level of performance on these tasks.

We identified in our study a pattern of ERP activity with promising sensitivity and specificity that may be able to identify individuals who are likely to develop AD later in life. This potential finding is especially relevant with the advent of treatments that may ameliorate the effects of AD if applied early in its course or even prevent the disease. The pattern of ERP activity that best aided in the discrimination of the PSEN1 carriers involved left posterior regions and left frontal regions. Structures in these regions have long been implicated in AD31,32 and atrophy in these structures has been found to be predictive of disease progression.3335 The sensitivity and specificity of our results are comparable to studies using other ERP measures8,36 as potential markers of preclinical AD. Thus, our analysis reveals a possible cognitive marker that may potentially aid early diagnosis, which needs to be confirmed with much larger population-based studies. In addition, future research is needed to determine whether ERP brain correlates as preclinical markers of AD may translate from familial to sporadic forms of the disease.


The authors thank the Grupo de Neurociencias de Antioquia staff for assistance with data acquisition; Dr. Robert Ross from the BU Center for Memory and Brain for comments on the manuscript; and the PSEN1 Colombian families for contributing their time and effort.


Alzheimer disease
amnestic mild cognitive impairment
analysis of variance
Consortium to Establish a Registry for Alzheimer's Disease
event-related potential
familial AD
left anterior inferior
left posterior superior
region of interest


Y.T.Q., B.A.A., C.E.S., and A.E.B. designed experiments. Y.T.Q., A.L.R., and J.M. performed experiments. Y.T.Q., B.A.A., K.C., F.L., C.E.S., and A.E.B. analyzed and interpreted data. F.L. supervised work in Colombia. A.E.B. and C.E.S. supervised work in Boston. Y.T.Q., B.A.A., and A.E.B. wrote the manuscript.


Y.T. Quiroz reports no disclosures. Dr. Ally receives research support from the NIH/NIA and Vanderbilt University. K. Celone, J. McKeever, A.L. Ruiz-Rizzo, and Dr. Lopera report no disclosures. Prof. Stern serves on the editorial boards of Frontiers in Neuroscience, Behavioral Neuroscience, and Hippocampus; and receives research support from the NIH (NINDS/ NIMH), the Office of Naval Research, and the National Science Foundation. Dr. Budson serves on the editorial boards of Reviews in Neurological Diseases, the Journal of Medicine, and the International Journal of Alzheimer's Disease; serves as Deputy Chief of Staff for the VA Boston Healthcare System; and receives research support from the NIH/NIA.


1. Zhou Y, Dougherty JH, Jr, Hubner KF, Bai B, Cannon RL, Hutson RK. Abnormal connectivity in the posterior cingulate and hippocampus in early Alzheimer's disease and mild cognitive impairment. Alzheimers Dement 2008;4:265–270 [PubMed]
2. Papaliagkas V, Kimiskidis V, Tsolaki M, Anogianakis G. Usefulness of event-related potentials in the assessment of mild cognitive impairment. BMC Neurosci 2008;9:107. [PMC free article] [PubMed]
3. Raggi A, Consonni M, Iannaccone S, et al. Auditory event-related potentials in non-demented patients with sporadic amyotrophic lateral sclerosis. Clin Neurophysiol 2008;119:342–350 [PubMed]
4. Bennys K, Portet F, Touchon J, Rondouin G. Diagnostic value of event-related evoked potentials N200 and P300 subcomponents in early diagnosis of Alzheimer's disease and mild cognitive impairment. J Clin Neurophysiol 2007;24:405–412 [PubMed]
5. Bokura H, Yamaguchi S, Kobayashi S. Event-related potentials for response inhibition in Parkinson's disease. Neuropsychologia 2005;43:967–975 [PubMed]
6. Ally BA, Waring JD, Beth EH, McKeever JD, Milberg WP, Budson AE. Aging memory for pictures: using high-density event-related potentials to understand the effect of aging on the picture superiority effect. Neuropsychologia 2008;46:679–689 [PMC free article] [PubMed]
7. Ally BA, McKeever JD, Waring JD, Budson AE. Preserved frontal memorial processing for pictures in patients with mild cognitive impairment. Neuropsychologia 2009;47:2044–2055 [PMC free article] [PubMed]
8. Golob EJ, Ringman JM, Irimajiri R, et al. Cortical event-related potentials in preclinical familial Alzheimer disease. Neurology 2009;73:1649–1655 [PMC free article] [PubMed]
9. Bobes MA, Garcia YF, Lopera F, et al. ERP generator anomalies in presymptomatic carriers of the Alzheimer's disease E280A PS-1 mutation. Hum Brain Mapp 2010;31:247–265 [PubMed]
10. Olichney JM, Iragui VJ, Salmon DP, Riggins BR, Morris SK, Kutas M. Absent event-related potential (ERP) word repetition effects in mild Alzheimer's disease. Clin Neurophysiol 2006;117:1319–1330 [PMC free article] [PubMed]
11. Missonnier P, Gold G, Fazio-Costa L, et al. Early event-related potential changes during working memory activation predict rapid decline in mild cognitive impairment. J Gerontol A Biol Sci Med Sci 2005;60:660–666 [PubMed]
12. Katada E, Sato K, Ojika K, Ueda R. Cognitive event-related potentials: useful clinical information in Alzheimer's disease. Curr Alzheimer Res 2004;1:63–69 [PubMed]
13. Husain MM, Trevino K, Siddique H, McClintock SM. Present and prospective clinical therapeutic regimens for Alzheimer's disease. Neuropsychiatr Dis Treat 2008;4:765–777 [PMC free article] [PubMed]
14. Reiman EM, Langbaum JB, Tariot PN. Alzheimer's prevention initiative: a proposal to evaluate presymptomatic treatments as quickly as possible. Biomark Med 2010;4:3–14 [PMC free article] [PubMed]
15. Lleo A, Berezovska O, Growdon JH, Hyman BT. Clinical, pathological, and biochemical spectrum of Alzheimer disease associated with PS-1 mutations. Am J Geriatr Psychiatry 2004;12:146–156 [PubMed]
16. Rosselli MC, Ardila AC, Moreno SC, et al. Cognitive decline in patients with familial Alzheimer's disease associated with E280a presenilin-1 mutation: a longitudinal study. J Clin Exp Neuropsychol 2000;22:483–495 [PubMed]
17. Ardila A, Lopera F, Rosselli M, et al. Neuropsychological profile of a large kindred with familial Alzheimer's disease caused by the E280A single presenilin-1 mutation. Arch Clin Neuropsychol 2000;15:515–528 [PubMed]
18. Gomez-Isla T, Wasco W, Pettingell WP, et al. A novel presenilin-1 mutation: increased beta-amyloid and neurofibrillary changes. Ann Neurol 1997;41:809–813 [PubMed]
19. Lopera F, Ardilla A, Martinez A, et al. Clinical features of early-onset Alzheimer disease in a large kindred with an E280A presenilin-1 mutation. JAMA 1997;277:793–799 [PubMed]
20. Scahill RI, Schott JM, Stevens JM, Rossor MN, Fox NC. Mapping the evolution of regional atrophy in Alzheimer's disease: unbiased analysis of fluid-registered serial MRI. Proc Natl Acad Sci USA 2002;99:4703–4707 [PubMed]
21. Schott JM, Fox NC, Frost C, et al. Assessing the onset of structural change in familial Alzheimer's disease. Ann Neurol 2003;53:181–188 [PubMed]
22. Johnson KA, Lopera F, Jones K, et al. Presenilin-1-associated abnormalities in regional cerebral perfusion. Neurology 2001;56:1545–1551 [PubMed]
23. Mondadori CR, Buchmann A, Mustovic H, et al. Enhanced brain activity may precede the diagnosis of Alzheimer's disease by 30 years. Brain 2006;129:2908–2922 [PubMed]
24. Quiroz YT, Budson AE, Celone K, et al. Hippocampal hyperactivation in presymptomatic familial Alzheimer's disease. Ann Neurol 2010;68:865–875 [PMC free article] [PubMed]
25. Ringman JM, O'Neill J, Geschwind D, et al. Diffusion tensor imaging in preclinical and presymptomatic carriers of familial Alzheimer's disease mutations. Brain 2007;130:1767–1776 [PubMed]
26. Aguirre-Acevedo DC, Gomez RD, Moreno S, et al. [Validity and reliability of the CERAD-Col neuropsychological battery. ] Rev Neurol 2007;45:655–660 [PubMed]
27. Ally BA, Budson AE. The worth of pictures: using high density event-related potentials to understand the memorial power of pictures and the dynamics of recognition memory. Neuroimage 2007;35:378–395 [PMC free article] [PubMed]
28. Cronin-Golomb A, Gilmore GC, Neargarder S, Morrison SR, Laudate TM. Enhanced stimulus strength improves visual cognition in aging and Alzheimer's disease. Cortex 2007;43:952–966 [PubMed]
29. Trollor JN, Sachdev PS, Haindl W, Brodaty H, Wen W, Walker BM. A high-resolution single photon emission computed tomography study of verbal recognition memory in Alzheimer's disease. Dement Geriatr Cogn Disord 2006;21:267–274 [PubMed]
30. Cummings JL. Cognitive and behavioral heterogeneity in Alzheimer's disease: seeking the neurobiological basis. Neurobiol Aging 2000;21:845–861 [PubMed]
31. McKee AC, Au R, Cabral HJ, et al. Visual association pathology in preclinical Alzheimer disease. J Neuropathol Exp Neurol 2006;65:621–630 [PubMed]
32. Chetelat G, Villemagne VL, Bourgeat P, et al. Relationship between atrophy and beta-amyloid deposition in Alzheimer disease. Ann Neurol 2010;67:317–324 [PubMed]
33. McEvoy LK, Fennema-Notestine C, Roddey JC, et al. Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. Radiology 2009;251:195–205 [PubMed]
34. Sluimer JD, van der Flier WM, Karas GB, et al. Accelerating regional atrophy rates in the progression from normal aging to Alzheimer's disease. Eur Radiol 2009;19:2826–2833 [PMC free article] [PubMed]
35. Karas G, Sluimer J, Goekoop R, et al. Amnestic mild cognitive impairment: structural MR imaging findings predictive of conversion to Alzheimer disease. AJNR Am J Neuroradiol 2008;29:944–949 [PubMed]
36. Chapman RM, Nowlis GH, McCrary JW, et al. Brain event-related potentials: diagnosing early-stage Alzheimer's disease. Neurobiol Aging 2007;28:194–201 [PMC free article] [PubMed]

Articles from Neurology are provided here courtesy of American Academy of Neurology