This article appeared in a journal published by Elsevier. The attachedcopy is furnished to the author for internal non-commercial researchand education use, including for instruction at the authors institution

and sharing with colleagues.

Other uses, including reproduction and distribution, or selling orlicensing copies, or posting to personal, institutional or third party

websites are prohibited.

In most cases authors are permitted to post their version of thearticle (e.g. in Word or Tex form) to their personal website orinstitutional repository. Authors requiring further information

regarding Elsevier’s archiving and manuscript policies areencouraged to visit:

http://www.elsevier.com/copyright

Author's personal copy

Neurobiology of Aging 32 (2011) 1733–1741

Review

Neurostructural predictors of Alzheimer’s disease:A meta-analysis of VBM studies

Luiz K. Ferreira a,∗, Breno S. Diniz b, Orestes V. Forlenza b,Geraldo F. Busatto a, Marcus V. Zanetti a

a Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry,Faculty of Medicine, University of São Paulo, São Paulo, SP, Brazil

b Laboratory of Neurosciences (LIM-27), Department and Institute of Psychiatry,Faculty of Medicine, University of São Paulo, São Paulo, SP, Brazil

Received 22 June 2009; received in revised form 16 October 2009; accepted 10 November 2009Available online 11 December 2009

Abstract

The identification of biological markers at early stages of Alzheimer’s disease (AD) contributes to diagnostic accuracy and adds prognosticvalue. However, in spite of recent developments, results of neurostructural imaging studies on predicting conversion to AD are not uniform.We conducted a systematic review of voxel-based morphometry (VBM) studies about the neurostructural predictors of conversion to AD.Ten studies met inclusion criteria and nine reported baseline regional gray matter (GM) atrophy in mild cognitive impairment (MCI) orhealthy subjects who progressed to AD. Using the method of Activation Likelihood Estimation, we meta-analyzed the coordinates from thesix longitudinal VBM studies that enrolled subjects with amnestic MCI (aMCI) at baseline. These comprised a total of 429 aMCI subjects,of which 142 converted to AD. Meta-analysis yielded one significant cluster of GM volumetric reduction in aMCI patients who convertedto AD, located in the left hippocampus and parahippocampal gyrus. In conclusion, left medial temporal lobe atrophy is the most consistentneurostructural biomarker to predict conversion from aMCI to AD.© 2009 Elsevier Inc. All rights reserved.

Keywords: Alzheimer’s disease; Mild cognitive impairment; MRI; Voxel-based morphometry; Systematic review; Meta-analysis; Activation likelihoodestimation

1. Introduction

The early diagnosis of Alzheimer’s disease (AD) and therecognition of subjects at higher risk to develop dementiaare important, as both treatment and preventive interven-tions aiming at the modification of the disease processwill be more efficacious if started at pre-dementia stages(Cummings et al., 2007). The definition of mild cogni-tive impairment (MCI) has been widely used to identify

∗ Corresponding author at: Centro de Medicina Nuclear, 3◦ andar, LIM-21, Rua Dr. Ovídio Pires de Campos, s/n, Postal Code 05403-010, São Paulo,SP, Brazil. Tel.: +55 11 30698193; fax: +55 11 30821015.

E-mail addresses: kobuti@yahoo.com (L.K. Ferreira),breno satler@usp.br (B.S. Diniz), forlenza@usp.br (O.V. Forlenza),gbusatto@terra.com.br (G.F. Busatto), marcus zanetti@yahoo.com.br(M.V. Zanetti).

individuals presenting with the cognitive signs and symp-toms of incipient AD or at higher risk to progress toclinical dementia stages (Petersen, 2004). Although sub-jects with MCI have an overall increased risk to progressto AD/dementia when compared to cognitively unimpairedsubjects, the diagnostic criteria of MCI may lack diagnos-tic specificity, hampering its prognostic value (Forlenza andChiu, 2008).

In fact, population-based studies and cohorts derived froma tertiary center illustrated variable rates of conversion to ADand a poor predictive value of MCI subtypes to predict distinct(non-AD) dementia outcomes (Busse et al., 2006; Farias etal., 2009; Mitchell and Shiri-Feshki, 2008). Recent studiesfrom our group have also shown that even amnestic MCI(aMCI), which is considered indicative of medial temporallobe dysfunction, may have a bi-directional outcome, i.e.,

0197-4580/$ – see front matter © 2009 Elsevier Inc. All rights reserved.doi:10.1016/j.neurobiolaging.2009.11.008

Author's personal copy

1734 L.K. Ferreira et al. / Neurobiology of Aging 32 (2011) 1733–1741

both progression to AD and full or partial cognitive recoveryupon follow-up (Diniz et al., 2009; Forlenza et al., in press).

The combination of clinical and biological information,as provided by genetic, biochemical and neuroimaging cor-relates of the AD pathogenesis, improves the prognosticaccuracy of the MCI diagnosis. The most promising biomark-ers are the cerebro-spinal fluid (CSF) concentrations ofamyloid-�42, total Tau and phosphorylated Tau (Diniz etal., 2008), apolipoprotein E-�4 carrier status (Mahley et al.,2006), volumetry of the medial temporal lobe based on struc-tural magnetic resonance imaging (MRI) (Ries et al., 2008),fluorodeoxyglucose PET imaging (Jagust et al., 2007), and invivo labeling of amyloid-�42 and Tau protein through molec-ular imaging techniques (Small et al., 2006).

Morphometric MRI is a safe, non-invasive and reliablemethod that can be used to improve diagnostic accuracy andto monitor disease progression in AD (Busatto et al., 2008).The most commonly employed techniques to address mor-phometric changes in the brain are the visual rating of regionalatrophy, analyses based on region-of-interest (ROI) measure-ments, and voxel-based morphometry (VBM) (Busatto etal., 2008). The medial temporal lobe is a region of majorinterest for the early diagnosis of AD, as the core patholog-ical changes observed in AD take place in this brain region,and initiate years before the first clinical manifestations ofdementia (Chetelat and Baron, 2003). Longitudinal studieshave shown that atrophy of medial temporal lobe structures(e.g. hippocampus) is a strong and independent predictor ofprogression from pre-dementia stages to AD (Ries et al.,2008). However, recent reviews on MRI studies regardingprediction of conversion have not always been concordant(Busatto et al., 2008; Ries et al., 2008). This has supportedsuggestions that the early diagnosis of AD based on struc-tural neuroimaging should not rely solely on medial temporallobe measurements, but also on morphometric changes inother brain areas (Chetelat and Baron, 2003). The methodemployed for image analysis can also influence the resultsof MRI studies (Busatto et al., 2008; Testa et al., 2004).Major limitations of ROI-based and visual evaluation tech-niques of morphometric brain changes are that these methodsrequire a priori decisions concerning which structures toevaluate (Busatto et al., 2008). Moreover, in ROI-based meth-ods, regions showing abnormal gray matter (GM) volumemight be part of a large ROI, or spread over different ROIs,thereby potentially reducing statistical power of the underly-ing morphological analysis (Fan et al., 2007). Due to theselimitations, an advantageous alternative to ROI-based studiesis VBM.

Briefly, the VBM technique consists of: (1) spatial nor-malization, i.e., transformation of all MRI data to the samestereotactic space, thus correcting for individual brain shapedifferences; (2) segmentation of brain tissue into GM, whitematter and CSF based on voxel intensities; (3) smoothing ofthe image in order to increase the validity of the statisticalanalysis and finally (4) comparison of the segment of inter-est (e.g. gray matter) between groups on a voxel-by-voxel

basis, resulting in statistical maps that provide the locationand statistical significance values of the regions where differ-ences in volumes are present (Ashburner and Friston, 2000;Ashburner, 2009). Thus, in contrast to ROI-based methods,VBM analysis is not restricted to previously defined ROIs—itis possible to investigate the presence of between-group dif-ferences in GM volumes across the whole brain. Moreover,VBM is an automated, rater-independent method of imagingdata analysis, and, thus, provides highly reproducible results(Busatto et al., 2008). For instance, one study found VBM tobe more accurate than ROI-based volumetry in discriminat-ing AD patients from controls, and also to detect hippocampalatrophy in AD individuals (Testa et al., 2004).

A recent meta-analysis of neuroimaging studies usingvoxel-based image analysis methods comparing baselineneuroimaging data of stable MCI subjects and patients withMCI who converted to AD found clusters of significant dif-ferences between these groups in the inferior parietal lobeand precuneus (Schroeter et al., 2009). However, this meta-analysis has pooled data from PET, SPECT and MRI studiesto perform the statistical analysis and this may make theirresults difficult to interpret. Moreover, out of the seven arti-cles that directly compared converters to AD and stable MCIsubjects, in only two (Bozzali et al., 2006; Chetelat et al.,2005) morphometric MRI data were available. Finally, thesystematic review by Schroeter et al. (2009) encompassedarticles that were published until February 2007, and sincethen, further morphometric MRI studies with larger sampleshave appeared in the literature.

Due to the availability of recent results and to the lackof concordance among different studies, our purpose is tosystematically review and meta-analyze the VBM studies ofneurostructural predictors of conversion to AD.

2. Methods

2.1. Data sources and study selection

We carried out a systematic and comprehensive searchusing MEDLINE (http://www.ncbi.nlm.nih.gov/pubmed/),ISI Web of Science (www.isiknowledge.com), PsycInfo(http://www.apa.org/psycinfo/), EMBASE (www.embase.com) and SCOPUS (www.scopus.com) databases for VBMstudies investigating samples of patients with MCI or AD.The following Boolean phrase: [(Alzheimer OR “mild cog-nitive impairment” OR mci) AND (voxel OR vbm)] was used.The search was conducted in September 18, 2009 and no timespan was specified for date of publication. The titles andabstracts of the results of this search were carefully exam-ined in order to select those articles that would potentiallymeet our entry criteria. Full texts of these articles were sub-sequently retrieved in order to be verified for acceptance andinclusion into the present analysis. The reference sections ofthe selected articles were also carefully searched in order tocapture any additional pertinent references.

Author's personal copy

L.K. Ferreira et al. / Neurobiology of Aging 32 (2011) 1733–1741 1735

Studies were included if they: (1) performed baselinestructural brain MRI of normal subjects or MCI patients,(2) followed subjects to determine which of them convertedto AD, (3) compared baseline MRI of normal or MCI sub-jects who converted to AD (not to other dementia types) withbaseline MRI of non-converters, (4) performed whole-brainanalysis and (5) followed, in general, the VBM protocolsdescribed by Ashburner and Friston (2000) or Good et al.(2001).

2.2. Meta-analytic procedures

In order to allow us to perform the meta-analytical pro-cedure, we retrieved the reported stereotactic coordinatesrelated to the comparisons between baseline MRI of con-verters and non-converters from every selected study. In caseof articles reporting sets of coordinates derived from dif-ferent procedures of data analysis (e.g. correcting or notfor multiple comparisons in statistical analysis), we selectedthe coordinates derived from the most statistically stringentmethod. If the article presented the results as figures, weasked the corresponding author for the stereotactic coordi-nates.

Meta-analysis was based on the activation likelihood esti-mation (ALE) method (Turkeltaub et al., 2002), using therevised ALE algorithm (Eickhoff et al., 2009) in Ginger Ale2.0 program (Laird et al., 2005). The ALE is a method forperforming coordinate-based meta-analysis in order to deter-mine if there is anatomical convergence among results fromdifferent studies. Every coordinate represented the center of athree-dimensional Gaussian probability distribution. The fullwidth at half maximum (FWHM) for the Gaussian probabilitydistributions was calculated in accordance with a quantita-tive uncertainty model as described by Eickhoff et al. (2009).Briefly, it assumes that studies with larger samples presentresults (coordinates) with greater spatial accuracy. Since theFWHM is used to model the uncertainty in spatial location

of the reported foci, smaller FWHM values are used for focifrom larger studies.

For each study, the three-dimensional Gaussian proba-bility distributions calculated from the coordinates of thisindividual study were summed as a modeled activation (MA)volume. Thus, one MA map was computed for each study.The voxel-wise union of MA maps of all included studiesresulted in the ‘true ALE scores’—the probability that at leastone of the anatomical foci lies within a given voxel.

This revised ALE meta-analytic approach aims to indicatethe anatomical convergence between results from differentstudies, thus distinguishing it from random spatial associa-tion between studies (Eickhoff et al., 2009). For this reason,a null-distribution (i.e. noise) is calculated as follows. Onerandom voxel is independently drawn from each MA mapand the probability of each voxel (the value of MA mapsat the selected voxel) of this random sample is computed.The union of these probabilities yields a ‘random ALEscore’. By repeating this procedure, a random spatial asso-ciation between the studies is performed and an empiricalnull-distribution is constructed. The ‘true ALE scores’ werethen assessed against this null-distribution of ‘random ALEscores’ in order to establish statistical significance (Eickhoffet al., 2009).

The map of final ALE scores was thresholded with a falsediscovery rate at p < 0.01 and a cluster extent threshold of100 mm3. Significant clusters were overlaid onto an anatom-ical MNI template, Colin27 T1 seg MNI.nii (available atwww.brainmap.org/ale/index.html), using the MRIcron soft-ware (http://www.sph.sc.edu/comd/rorden/mricron/).

We further meta-analyzed clinical (scores on the Mini-Mental State Examination) (Folstein et al., 1975) andsocio-demographic data (age, educational level and gender)for the studies included in the ALE meta-analysis using thesoftware “R: A Language and Environment for StatisticalComputing” version 2.4.1 (R Foundation for Statistical Com-puting, Vienna, Austria). If the studies did not report the

Table 1Clinical and demographic data from articles included in the meta-analysis.

Reference Subjects (n) Age (years)a Gender (F/M) Education (years)a MMSE score Follow-up (months)

Bozzali et al. (2006) C 14 71.8 ± 13.0 8/6 9.3 ± 3.5 25.8 ± 1.7 24S 8 71.4 ± 10.3 4/4 8.1 ± 4.3 24.8 ± 1.5 24

Chetelat et al. (2005) C 7 74.0 ± 4.6 4/3 10.6 ± 4.2 26.6 ± 1.0 18S 11 67.1 ± 8.9 7/4 9.4 ± 2.9 27.8 ± 1.2 18

Karas et al. (2008) C 11 72.2 ± 4.8 8/3 NA 25.9 ± 0.9 36S 13 72.4 ± 8.6 10/3 NA 27.5 ± 1.4 36

Risacher et al. (2009) C 62 74.3 ± 0.9 26/36 15.2 ± 0.4 26.7 ± 0.2 12S 277 75.1 ± 0.4 99/178 15.8 ± 0.2 27.1 ± 0.1 12

Waragai et al. (2009) C 6 80.2 ± 4.1 4/2 NA 25.7 ± 2.0 27.0 ± 7.9a

S 7 77.6 ± 3.1 2/5 NA 26.3 ± 1.1 27.7 ± 2.2a

Whitwell et al. (2008) C 42 79.2 ± 7.5 24/18 14.1 ± 3.3 25.3 ± 2.2 18S 21 80.0 ± 7.7 12/9 15.1 ± 3.6 27.1 ± 1.9 36

C, aMCI converters to AD. S, stable aMCI. NA, data not available.a Mean ± s.d.

Author's personal copy

1736 L.K. Ferreira et al. / Neurobiology of Aging 32 (2011) 1733–1741

Table 2Locia of gray matter reduction in aMCI converters to AD as compared to stable aMCI in each of the 5 enrolled studies which reported significant resultsb.

Reference Bozzali et al. (2006) Chetelat et al. (2005) Karas et al. (2008) Risacher et al. (2009) Whitwell et al. (2008)

Gaussian kernel (mm)c 12 8 12 10 8Statistical thresholdd puncorr < 0.001, k ≥ 50 pcluster < 0.05, k > 500 pcluster = 0.1,

pfdr = 0.1, k = 70pfdr ≤ 0.005, k = 27 pfdr ≤ 0.005

Temporal lobeHippocampus, R One cluster: k = 112Hippocampus, L One cluster: k = 275 One cluster:

k = 168453Parahippoc. G., L One cluster: k = 6108Uncus, L One cluster: k = 93Amygdala, R One cluster: k = 156Inf. Temp. G., R One cluster: k = 1039Inf. Temp. G., L One cluster: k = 1288Mid. Temp. G., R One cluster: k = 82

Mid. Temp. G., L One cluster: k = 941 Four clusters:1. k = 4152. k = 2123. k = 684. k = 29

Sup. Temp. G., R One cluster: k = 733Trans. Temp. G., L One cluster: k = 54

Frontal lobeSup. Frontal G., R Two clusters:

1. k = 7192. k = 676

Sup. Frontal G., L One cluster: k = 40

Inf. Frontal G., R Two clusters:1. k = 2032. k = 88

Inf. Frontal G., L One cluster: k = 54

Medial Frontal G., R Two clusters:1. k = 1832. k = 33

Precentral G., R One cluster: k = 162

Parietal lobeInf. Parietal Lobule, L One cluster: k = 931

Inf. Parietal Lobule, R Two clusters:k = 1565k = 37

Occiptal lobeLingual G., R One cluster: k = 4499

OtherInsula, R Two clusters:

1. k = 45772. k = 354

G., gyrus. Inf, Inferior. L, Left. Mid, Middle. Parahippoc, Parahippocampal. pfdr, p value corrected for multiple comparisons using false discovery rate. puncorr,p value uncorrected for multiple comparisons. R, Right. Sup, Superior. Temp, Temporal. Trans, Transverse.

a The anatomical label is based on the peak stereotactic coordinates from each significant cluster provided by each study. The number of voxels within eachcluster (k) is presented.

b The study of Waragai et al. (2009) did not find any significant cluster (Gaussian kernel of 12 mm; statistical threshold: p-family wise error corrected < 0.05,k = 100).

c Size of the full width at half maximum smoothing kernel that each study employed in image processing (millimeters).d Statistical threshold employed by each study.

Author's personal copy

L.K. Ferreira et al. / Neurobiology of Aging 32 (2011) 1733–1741 1737

necessary data, we asked the corresponding author for thisinformation.

3. Results

The search strategy identified a total of 742 studies. Tenarticles (Bell-McGinty et al., 2005; Bozzali et al., 2006; Bryset al., 2009; Chetelat et al., 2005; Hall et al., 2008; Karaset al., 2008; Morbelli et al., in press; Risacher et al., 2009;Waragai et al., 2009; Whitwell et al., 2008) met the inclusioncriteria. All studies but one (Waragai et al., 2009) showed thatsubjects who had progressed to AD in the follow-up presentedregional GM reductions at baseline when compared to thosewho did not progress to AD. No study reported deficits inboth normal (cognitively preserved) or MCI subjects whoremained stable over time (“non-converters”) compared tothose who developed AD. No additional papers were foundafter searching the reference sections of the selected articles.

Six studies enrolled, at baseline, subjects with aMCI (inaccordance to the generally accepted criteria for aMCI aspublished by Petersen, 2004); the four other studies included:patients with more than one MCI subtype (Bell-McGinty etal., 2005; Morbelli et al., in press), patients with non-specifiedMCI (Brys et al., 2009) and normal subjects (Hall et al.,2008).

In order to maximize the homogeneity of samples selectedfor meta-analysis, we included in the meta-analytic processonly the six studies including aMCI subjects (Bozzali et al.,2006; Chetelat et al., 2005; Karas et al., 2008; Risacher etal., 2009; Waragai et al., 2009; Whitwell et al., 2008). Theseenrolled a total of 429 aMCI subjects (of which 142 convertedto AD) and comprised a total of 31 coordinates that were com-puted in the Ginger Ale 2.0. as foci to estimate ALE values.Two studies (Risacher et al., 2009; Whitwell et al., 2008) did

not publish the stereotactic coordinates but these were pro-vided by the authors after request (personal communication).The clinical and demographic data from articles included inthe meta-analysis are presented in Table 1.

We found no significant difference between MCI-stableand MCI converter on age (SMD = 0.08, CI95% (−1.3; 1.5),z = 0.11, p = 0.9), educational level (SMD = −0.5, CI95%(−1.2; 0.2), z = −1.31, p = 0.2) and gender distribution(RR = 0.9, CI95% (0.7; 1.1), z = −1.03, p = 0.3). On the otherhand, the cognitive performance of MCI converters wassignificantly worse than MCI-stable patients (SMD = −0.85CI95% (−1.6; −0.06), z = −2.12, p = 0.03). The educationallevel of studied subjects was not provided in two studies(Karas et al., 2008; Waragai et al., 2009); therefore, the meta-analysis of years of education was performed without theinformation from these studies.

Table 2 provides the regions of GM reduction in aMCIsubjects who subsequently converted to AD as compared tostable aMCI patients found by the studies included in themeta-analysis. One study (Waragai et al., 2009) was includedin the meta-analysis but did not find any significant cluster andtherefore is not listed in Table 2. Most of the regions relatedto GM deficits in aMCI subjects who eventually progressedto AD were located in the temporal lobe, particularly on theleft side. The regions of GM atrophy within the temporallobe varied among studies and included the hippocampus,the parahippocampal gyrus, the amygdala, the uncus and theinferior, middle, superior and transverse temporal gyri.

After performing the meta-analysis (p < 0.01, false discov-ery rate corrected; cluster extent threshold of 100 mm3), wefound one significant cluster of volumetric decrease in aMCIpatients who converted to AD in a short time as compared tothose who did not. This 128 mm3 cluster was located in theanterior hippocampus and parahippocampal gyrus on the leftside (peak voxel at MNI coordinate −28,−12,−18) (Fig. 1).

Fig. 1. Result of meta-analysis: region of structural gray matter reduction in aMCI subjects who developed AD compared to stable aMCI. Cluster of volumetricgray matter deficits in aMCI who converted to AD as resulting from ALE meta-analysis (p < 0.01, false discovery rate corrected; cluster extent threshold of100 mm3): left hippocampus and parahippocampal gyrus in coronal (y = −10) and axial (z = −19) slices.

Author's personal copy

1738 L.K. Ferreira et al. / Neurobiology of Aging 32 (2011) 1733–1741

The report of results presented by Ginger Ale 2.0 indicatedthat two studies (Risacher et al., 2009; Whitwell et al., 2008)provided the coordinates which contributed the most to thissignificant cluster.

4. Discussion

The present results corroborate the notion that MCI indi-viduals who will convert to AD present a pattern of regionalGM abnormalities distinct from those subjects who will notprogress to dementia upon follow-up. Our meta-analysisof VBM-based work strengthens the notion that baselineatrophy of mesial temporal lobe structures is an impor-tant predictor of development of full-blown AD in patientswith aMCI, especially the hippocampus and parahippocam-pal gyrus on the left hemisphere. As we found no significantdifferences in socio-demographic data between the MCI con-verter vs. MCI-stable patients, the present findings of reducedvolumes in the anterior hippocampus and parahippocampalgyrus on the left side may not have been biased by potentialconfounders, such as age and gender.

Reviews about the neurostructural predictors of conver-sion from MCI to AD have found difficulties in comparingresults across studies due to methodological heterogeneity,especially regarding image processing and analysis tech-niques, and heterogeneous characterization of MCI patients(Busatto et al., 2008; Chetelat and Baron, 2003). In spite ofthese restrictions, atrophic changes in temporal lobe struc-tures, involving both the medial temporal lobe and temporalneocortex, have been the most consistent findings in bothMCI and AD individuals, even at an early stage of theirdisease (Busatto et al., 2008; Chetelat and Baron, 2003).Patients with MCI that progress to AD show more intensebaseline atrophy and higher hippocampal atrophy rates ascompared to controls or MCI-stable patients. These initialchanges may be present years before the clinical manifesta-tions of dementia and are more prominent on the left side ofthe brain (Apostolova et al., 2006; Jack et al., 2004; Scahillet al., 2002; Thompson et al., 2003; Whitwell et al., 2007).In addition to the higher degree of hippocampal atrophy inMCI patients when compared to healthy controls, atrophyis also greater in AD than in MCI, suggesting that the atro-phy of the hippocampus occurs throughout the initial courseof the disease (Du et al., 2001; Ries et al., 2008; Whitwellet al., 2007). The results of these neuroimaging studies arein agreement with the neuropathology of AD. Structures inthis region are known to be the first affected by AD pathol-ogy and neuropathological features of AD are expected tobe found in this region during the preclinical stages and tobecome more severe over the course of disease (Braak et al.,1999).

In accordance with the above findings, our meta-analysisidentified volumetric decreases in the left hippocampus andparahippocampal gyrus as the structural GM abnormalitiesmost consistently associated with the conversion to AD

in aMCI individuals. This result suggests that those whopresent greater degree of atrophy in the left hippocam-pus/parahippocampal gyrus are more likely to develop ADupon a short follow-up time. However, based on our results,it is not possible to determine whether the volumetric deficitsobserved in these left medial temporal regions are related toa longer duration of disease or by a more aggressive neurode-generative process.

The significant cluster was a result of the combination ofthe three-dimensional distributions of probabilities that werecentered at each computed stereotactic coordinate (Lairdet al., 2005). It is worth mentioning that, though all 31coordinates from the six included articles were computedin order to determine the map of “true ALE scores” andalso to provide information to establish the null-hypothesis,the report of results indicated that only two studies con-tributed to the significant cluster, indicating a low degreeof concordance between studies. This is in agreement withthe set of data presented in Table 2, which shows that thebrain regions where the selected studies found peak voxelsof significant clusters were not homogenous. Nevertheless,although there were coordinates in other important regions(e.g. left temporal neocortex), the combination of proba-bilities resulting from these coordinates did not reach thestatistical threshold. Therefore, although all loci listed inTable 2 may represent important results, the meta-analysisprovides the most consistent regional GM abnormalitiesacross studies.

The results of our meta-analysis are different from those ofa review of VBM studies that used the ALE method to analyzepooled data from both functional and structural neuroimagingstudies (Schroeter et al., 2009). This study found significantclusters in the left inferior parietal lobe and right precuneus.However, as the authors of this paper indicate, these clus-ters are mainly due to the results from brain perfusion andglucose metabolism studies. In contrast, we chose to specif-ically select only neurostructural data in order to achieve aclearer result. Such discrepancy between the regions of neu-rofunctional decline – mainly in posterior regions – and ofGM atrophy – most frequently located in the temporal lobe –has already been reported (Caroli et al., 2007; Villain et al.,2008). It has been proposed that it may be due to a disconnec-tion process, the ‘diaschisis’ hypothesis, according to whichthe atrophy in temporal lobe structures relates to functionaldeficits in distant regions, mainly due to white matter dis-ruption (Villain et al., 2008). Accordingly, the abnormalitiesreported by neurofunctional and neurostructural studies mayrepresent simultaneous but not identical neuropathologicalprocesses. Therefore, the contrasting results from both meta-analyses can be viewed as an evidence of complementaryprocesses in the pathophysiology of AD.

Because the clinically oriented diagnostic criteria forMCI yields a rather heterogeneous group of patients, theidentification of regional GM abnormalities indicating agreater risk of AD may have important clinical implica-tions. The current findings, alongside findings from CSF

Author's personal copy

L.K. Ferreira et al. / Neurobiology of Aging 32 (2011) 1733–1741 1739

biomarker and functional neuroimaging studies, corroboratethe view that subjects who will be eventually diagnosedwith AD present with a specific pattern of pathologicalchanges which resembles the brain changes observed inpatients with AD. This pathological pattern (i.e. low con-centrations of amyloid-�42 peptide and elevated Tau andPhospho-Tau in the CSF, hypometabolism of temporo-parietal cortex as assessed by fluorodeoxyglucose PET,volumetric deficits in temporal lobe structures, and corticalthinning), together with findings of episodic memory deficits,constitute a signature of the disease process (“AD signa-ture”), allowing the diagnosis of incipient dementia to bemade even in the absence of significant functional decline(Bakkour et al., 2009; Dubois et al., 2007). The inclusionof biomarkers, either individually or in association, as aselection criterion in studies of interventions with disease-modifying drugs, would allow the recruitment of a morehomogeneous sample of subjects with higher risk to developAD, thus enabling the selection of those individuals moreprone to benefit from such interventions (Cummings et al.,2007).

The ALE technique is a well-validated, automated methodof meta-analyzing data from multiple VBM studies usingthe reported stereotactic coordinates (Laird et al., 2005). Itwas initially used to meta-analyze data from neurofunctionalstudies, and now it is also widely used to process neurostruc-tural data (e.g. Glahn et al., 2008; Schroeter et al., 2009).It provides the location of concordant foci among differentstudies and allows the use of significance thresholds, thusproviding good objectivity for the results and statisticallydefensible conclusions (Eickhoff et al., 2009; Laird et al.,2005; Turkeltaub et al., 2002). This technique has alreadybeen employed in a number of previous meta-analysis of neu-roimaging studies, not only in Alzheimer’s disease (Schroeteret al., 2009), but also in other disorders such as fronto-temporal dementia (Schroeter et al., 2008), schizophrenia(Glahn et al., 2008) and attention deficit hyperactivity dis-order (Ellison-Wright et al., 2008). The Ginger Ale 2.0 thatwe used in this study is an upgraded version featuring twomethodological improvements: a more adequate solution todetermine the FWHM values and a higher specificity regard-ing the final results (Eickhoff et al., 2009).

Our findings should be interpreted in light of somemethodological limitations. VBM techniques are designedto investigate between-group differences and are not suit-able to detect abnormalities in individual cases (Ashburnerand Friston, 2000). Other methods, such as support vectormachine-based classifiers and semi-automatized hippocam-pal volumetric analyses, are under development and allowdiscrimination of MRI datasets at an individual basis (Fan etal., 2007; Busatto et al., 2008). Secondly, the ALE techniquedoes not take account of the different statistical properties ofeach coordinate (z scores and cluster sizes) for each paper andalso the chosen statistical thresholds of each study is not com-puted. This may bias the results. For example, coordinatesfrom large clusters were analyzed as if they had the same sta-

tistical power as coordinates from small clusters. Moreover,since only the peak coordinate of each cluster is computed,very large clusters – such as the one presented by the studyof Whitwell et al. (2008) – may be underrepresented in theanalysis. In addition, although all studies used VBM methods,there were variations in some important parameters (e.g. sizeof smoothing kernel, statistical thresholds). Such variationsreinforce the need of replicating findings across studies andsupport the importance of meta-analyses. Another limitationof our meta-analytic procedure is related to the scarce numberof studies included in analysis. As a result, only 31 coordi-nates were used as foci for the generation of ALE values. Thesmall number of studies and coordinates, in addition to thelack of anatomical convergence are related to the small sizeof significant cluster. Nevertheless, the inclusion in the ALEanalysis of studies restricted to single-domain amnestic MCIaffords a more homogeneous sample and, thus, strengthensthe present results. Due to the aforementioned limitations, itis still important to conduct more studies, with large samplesand longer follow-up periods, in order to improve our under-stating of the neurostructural predictors of progression frompre-dementia stages to AD.

5. Conclusion

In summary, this systematic review and meta-analysisshowed that neurostructural GM deficits are a consistent find-ing in baseline MRI scanning of subjects who will laterdevelop AD. The left medial temporal lobe is the mostaffected region, especially the hippocampus and parahip-pocampal gyrus. Meta-analysis of VBM studies showed thatatrophy in these regions is the most consistent neurostructuralmarker to predict progression from aMCI to AD.

Conflict of interest

The authors declare no conflicts of interest.

Acknowledgements

We thank Dr. Marco Bozzali, Dr. Andrew Saykin,Dr. Shannon Risacher, Dr. Clifford Jack and Dr. JenniferWhitwell for sharing unpublished results of their studies(Bozzali et al., 2006; Risacher et al., 2009; Whitwell et al.,2008).

Breno S. Diniz and Marcus V. Zanetti are funded byCAPES, Brazil. Geraldo F. Busatto is partially funded byCNPq, Brazil. Orestes V. Forlenza is partially funded byFAPESP, CNPq and the Alzheimer’s Association. The Lab-oratory of Neuroscience (LIM-27) receives financial supportfrom ABADHS (Associacão Beneficente Alzira Denise Her-zog da Silva).

Author's personal copy

1740 L.K. Ferreira et al. / Neurobiology of Aging 32 (2011) 1733–1741

References

Apostolova, L.G., Dutton, R.A., Dinov, I.D., Hayashi, K.M., Toga, A.W.,Cummings, J.L., Thompson, P.M., 2006. Conversion of mild cogni-tive impairment to Alzheimer disease predicted by hippocampal atrophymaps. Arch. Neurol. 63, 693–699.

Ashburner, J., Friston, K.J., 2000. Voxel-based morphometry—the methods.Neuroimage 11, 805–821.

Ashburner, J., 2009. Computational anatomy with the SPM software. Magn.Reson. Imag. 27, 1163–1174.

Bakkour, A., Morris, J.C., Dickerson, B.C., 2009. The cortical signature ofprodromal AD: regional thinning predicts mild AD dementia. Neurology72, 1048–1055.

Bell-McGinty, S., Lopez, O.L., Meltzer, C.C., Scanlon, J.M., Whyte, E.M.,Dekosky, S.T., Becker, J.T., 2005. Differential cortical atrophy in sub-groups of mild cognitive impairment. Arch. Neurol. 62, 1393–1397.

Bozzali, M., Filippi, M., Magnani, G., Cercignani, M., Franceschi, M., Schi-atti, E., Castiglioni, S., Mossini, R., Falautano, M., Scotti, G., Comi, G.,Falini, A., 2006. The contribution of voxel-based morphometry in stagingpatients with mild cognitive impairment. Neurology 67, 453–460.

Braak, E., Griffing, K., Arai, K., Bohl, J., Bratzke, H., Braak, H., 1999. Neu-ropathology of Alzheimer’s disease: what is new since A. Alzheimer?Eur. Arch. Psychiatry Clin. Neurosci. 249 (Suppl. 3), 14–22.

Brys, M., Glodzik, L., Mosconi, L., Switalski, R., De Santi, S., Pirraglia,E., Rich, K., Kim, B.C., Mehta, P., Zinkowski, R., Pratico, D., Wallin,A., Zetterberg, H., Tsui, W.H., Rusinek, H., Blennow, K., de Leon,M.J., 2009. Magnetic resonance imaging improves cerebrospinal fluidbiomarkers in the early detection of Alzheimer’s disease. J. AlzheimersDis. 16, 351–362.

Busatto, G.F., Diniz, B.S., Zanetti, M.V., 2008. Voxel-based morphometryin Alzheimer’s disease. Expert Rev. Neurother. 8, 1691–1702.

Busse, A., Hensel, A., Guhne, U., Angermeyer, M.C., Riedel-Heller, S.G.,2006. Mild cognitive impairment: long-term course of four clinical sub-types. Neurology 67, 2176–2185.

Caroli, A., Testa, C., Geroldi, C., Nobili, F., Barnden, L.R., Guerra, U.P.,Bonetti, M., Frisoni, G.B., 2007. Cerebral perfusion correlates of con-version to Alzheimer’s disease in amnestic mild cognitive impairment.J. Neurol. 254, 1698–1707.

Chetelat, G., Baron, J.C., 2003. Early diagnosis of Alzheimer’s disease:contribution of structural neuroimaging. Neuroimage 18, 525–541.

Chetelat, G., Landeau, B., Eustache, F., Mezenge, F., Viader, F., de la Sayette,V., Desgranges, B., Baron, J.C., 2005. Using voxel-based morphometryto map the structural changes associated with rapid conversion in MCI:a longitudinal MRI study. Neuroimage 27, 934–946.

Cummings, J.L., Doody, R., Clark, C., 2007. Disease-modifying therapiesfor Alzheimer disease: challenges to early intervention. Neurology 69,1622–1634.

Diniz, B.S., Pinto Junior, J.A., Forlenza, O.V., 2008. Do CSF total tau, phos-phorylated tau, and beta-amyloid 42 help to predict progression of mildcognitive impairment to Alzheimer’s disease? A systematic review andmeta-analysis of the literature. World J. Biol. Psychiatry 9, 172–182.

Diniz, B.S., Nunes, P.V., Yassuda, M.S., Forlenza, O.V., 2009. Diagnosis ofmild cognitive impairment revisited after one year. Preliminary resultsof a prospective study. Dement. Geriatr. Cogn. Disord. 27, 224–231.

Du, A.T., Schuff, N., Amend, D., Laakso, M.P., Hsu, Y.Y., Jagust, W.J.,Yaffe, K., Kramer, J.H., Reed, B., Norman, D., Chui, H.C., Weiner,M.W., 2001. Magnetic resonance imaging of the entorhinal cortex andhippocampus in mild cognitive impairment and Alzheimer’s disease. J.Neurol. Neurosurg. Psychiatry 71, 441–447.

Dubois, B., Feldman, H.H., Jacova, C., Dekosky, S.T., Barberger-Gateau,P., Cummings, J., Delacourte, A., Galasko, D., Gauthier, S., Jicha, G.,Meguro, K., O’Brien, J., Pasquier, F., Robert, P., Rossor, M., Salloway, S.,Stern, Y., Visser, P.J., Scheltens, P., 2007. Research criteria for the diag-nosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria.Lancet Neurol. 6, 734–746.

Eickhoff, S.B., Laird, A.R., Grefkes, C., Wang, L.E., Zilles, K., Fox,P.T., 2009. Coordinate-based activation likelihood estimation meta-

analysis of neuroimaging data: a random-effects approach based onempirical estimates of spatial uncertainty. Hum. Brain Mapp. 30,2907–2926.

Ellison-Wright, I., Ellison-Wright, Z., Bullmore, E., 2008. Structural brainchange in attention deficit hyperactivity disorder identified by meta-analysis. BMC Psychiatry 30, 51.

Fan, Y., Shen, D., Gur, R.C., Gur, R.E., Davatzikos, C., 2007. COMPARE:classification of morphological patterns using adaptive regional ele-ments. IEEE Trans. Med. Imag. 26, 93–105.

Farias, S.T., Mungas, D., Reed, B.R., Harvey, D., DeCarli, C., 2009.Progression of mild cognitive impairment to dementia in clinic- vscommunity-based cohorts. Arch. Neurol. 66, 1151–1157.

Folstein, M.F., Folstein, S.E., McHugh, P.R., 1975. “Mini-mental state”.A practical method for grading the cognitive state of patients for theclinician. J. Psychiatr. Res. 12, 189–198.

Forlenza, O.V., Chiu, E., 2008. Mild cognitive impairment: a concept readyto move on? Curr. Opin. Psychiatry 21, 529–532.

Forlenza, O.V., Diniz, B.S., Nunes, P.V., Memória, C.M., Yassuda, M.S., Gat-taz, W.F., in press. Diagnostic transitions in mild cognitive impairmentsubtypes. Int. Psychogeriatrics.

Glahn, D.C., Laird, A.R., Ellison-Wright, I., Thelen, S.M., Robinson, J.L.,Lancaster, J.L., Bullmore, E., Fox, P.T., 2008. Meta-analysis of graymatter anomalies in schizophrenia: application of anatomic likelihoodestimation and network analysis. Biol. Psychiatry 64, 774–781.

Good, C.D., Johnsrude, I.S., Ashburner, J., Henson, R.N., Friston, K.J.,Frackowiak, R.S., 2001. A voxel-based morphometric study of ageingin 465 normal adult human brains. Neuroimage 14, 21–36.

Hall, A.M., Moore, R.Y., Lopez, O.L., Kuller, L., Becker, J.T., 2008. Basalforebrain atrophy is a presymptomatic marker for Alzheimer’s disease.Alzheimers Dement. 4, 271–279.

Jack Jr., C.R., Shiung, M.M., Gunter, J.L., O’Brien, P.C., Weigand, S.D.,Knopman, D.S., Boeve, B.F., Ivnik, R.J., Smith, G.E., Cha, R.H., Tan-galos, E.G., Petersen, R.C., 2004. Comparison of different MRI brainatrophy rate measures with clinical disease progression in AD. Neurol-ogy 62, 591–600.

Jagust, W., Reed, B., Mungas, D., Ellis, W., Decarli, C., 2007. What does flu-orodeoxyglucose PET imaging add to a clinical diagnosis of dementia?Neurology 69, 871–877.

Karas, G., Sluimer, J., Goekoop, R., van der Flier, W., Rombouts, S.A.,Vrenken, H., Scheltens, P., Fox, N., Barkhof, F., 2008. Amnestic mildcognitive impairment: structural MR imaging findings predictive of con-version to Alzheimer disease. AJNR Am. J. Neuroradiol. 29, 944–949.

Laird, A.R., Fox, P.M., Price, C.J., Glahn, D.C., Uecker, A.M., Lancaster,J.L., Turkeltaub, P.E., Kochunov, P., Fox, P.T., 2005. ALE meta-analysis:controlling the false discovery rate and performing statistical contrasts.Hum. Brain Mapp. 25, 155–164.

Mahley, R.W., Weisgraber, K.H., Huang, Y., 2006. Apolipoprotein E4:a causative factor and therapeutic target in neuropathology, includingAlzheimer’s disease. Proc. Natl. Acad. Sci. U.S.A. 103, 5644–5651.

Mitchell, A.J., Shiri-Feshki, M., 2008. Temporal trends in the long termrisk of progression of mild cognitive impairment: a pooled analysis. J.Neurol. Neurosurg. Psychiatry 79, 1386–1391.

Morbelli, S., Piccardo, A., Villavecchia, G., Dessi, B., Brugnolo, A., Piccini,A., Caroli, A., Frisoni, G., Rodriguez, G., Nobili, F., in press. Map-ping brain morphological and functional conversion patterns in amnesticMCI: a voxel-based MRI and FDG-PET study. Eur. J. Nucl. Med. Mol.Imag.

Petersen, R.C., 2004. Mild cognitive impairment as a diagnostic entity. J.Intern. Med. 256, 183–194.

Ries, M.L., Carlsson, C.M., Rowley, H.A., Sager, M.A., Gleason, C.E.,Asthana, S., Johnson, S.C., 2008. Magnetic resonance imaging charac-terization of brain structure and function in mild cognitive impairment:a review. J. Am. Geriatr. Soc. 56, 920–934.

Risacher, S.L., Saykin, A.J., West, J.D., Shen, L., Firpi, H.A., McDonald,B.C., Alzheimer’s Disease Neuroimaging Initiative (ADNI), 2009. Base-line MRI predictors of conversion from MCI to probable AD in the ADNIcohort. Curr. Alzheimer Res. 6, 347–361.

Author's personal copy

L.K. Ferreira et al. / Neurobiology of Aging 32 (2011) 1733–1741 1741

Scahill, R.I., Schott, J.M., Stevens, J.M., Rossor, M.N., Fox, N.C., 2002.Mapping the evolution of regional atrophy in Alzheimer’s disease: unbi-ased analysis of fluid-registered serial MRI. Proc. Natl. Acad. Sci. U.S.A.99, 4703–4707.

Schroeter, M.L., Raczka, K., Neumann, J., von Cramon, D.Y., 2008. Neu-ral networks in frontotemporal dementia—a meta-analysis. Neurobiol.Aging 29, 418–426.

Schroeter, M.L., Stein, T., Maslowski, N., Neumann, J., 2009. Neural corre-lates of Alzheimer’s disease and mild cognitive impairment: a systematicand quantitative meta-analysis involving 1351 patients. Neuroimage 47,1196–1206.

Small, G.W., Kepe, V., Ercoli, L.M., Siddarth, P., Bookheimer, S.Y., Miller,K.J., Lavretsky, H., Burggren, A.C., Cole, G.M., Vinters, H.V., Thomp-son, P.M., Huang, S.C., Satyamurthy, N., Phelps, M.E., Barrio, J.R.,2006. PET of brain amyloid and tau in mild cognitive impairment. N.Engl. J. Med. 355, 2652–2663.

Testa, C., Laakso, M.P., Sabattoli, F., Rossi, R., Beltramello, A., Soininen,H., Frisoni, G.B., 2004. A comparison between the accuracy of voxel-based morphometry and hippocampal volumetry in Alzheimer’s disease.J. Magn. Reson. Imag. 19, 274–282.

Thompson, P.M., Hayashi, K.M., de Zubicaray, G., Janke, A.L., Rose, S.E.,Semple, J., Herman, D., Hong, M.S., Dittmer, S.S., Doddrell, D.M.,

Toga, A.W., 2003. Dynamics of gray matter loss in Alzheimer’s disease.J. Neurosci. 23, 994–1005.

Turkeltaub, P.E., Eden, G.F., Jones, K.M., Zeffiro, T.A., 2002. Meta-analysisof the functional neuroanatomy of single-word reading: method andvalidation. Neuroimage 16, 765–780.

Villain, N., Desgranges, B., Viader, F., de la Sayette, V., Mézenge, F., Lan-deau, B., Baron, J.C., Eustache, F., Chételat, G., 2008. Relationshipsbetween hippocampal atrophy, white matter disruption, and gray matterhypometabolism in Alzheimer’s disease. J. Neurosci. 28, 6174–6181.

Waragai, M., Okamura, N., Furukawa, K., Tashiro, M., Furumoto, S., Funaki,Y., Kato, M., Iwata, R., Yanai, K., Kudo, Y., Arai, H., 2009. Compar-ison study of amyloid PET and voxel-based morphometry analysis inmild cognitive impairment and Alzheimer’s disease. J. Neurol. Sci. 285,100–108.

Whitwell, J.L., Przybelski, S.A., Weigand, S.D., Knopman, D.S., Boeve,B.F., Petersen, R.C., Jack Jr., C.R., 2007. 3D maps from multiple MRIillustrate changing atrophy patterns as subjects progress from mild cog-nitive impairment to Alzheimer’s disease. Brain 130, 1777–1786.

Whitwell, J.L., Shiung, M.M., Przybelski, S.A., Weigand, S.D., Knopman,D.S., Boeve, B.F., Petersen, R.C., Jack Jr., C.R., 2008. MRI patterns ofatrophy associated with progression to AD in amnestic mild cognitiveimpairment. Neurology 70, 512–520.