NeuroImage. Multimodal classification of Alzheimer's disease and mild cognitive impairment

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1 NeuroImage 55 (2011) Contents lsts avalable at ScenceDrect NeuroImage journal homepage: Multmodal classfcaton of Alzhemer's dsease and mld cogntve mparment Daoqang Zhang a, Yapng Wang a,b, Lupng Zhou a, Hong Yuan a, Dnggang Shen a, and the Alzhemer's Dsease Neuromagng Intatve 1 a Department of Radology and BRIC, Unversty of North Carolna at Chapel Hll, NC 27599, USA b Department of Automaton, Northwestern Polytechncal Unversty, X'an, Shaanx Provnce, Chna artcle nfo abstract Artcle hstory: Receved 14 October 2010 Revsed 11 December 2010 Accepted 5 January 2011 Avalable onlne 12 January 2011 Keywords: Alzhemer's dsease (AD) MCI Multmodal classfcaton AD bomarkers CSF Effectve and accurate dagnoss of Alzhemer's dsease (AD), as well as ts prodromal stage (.e., mld cogntve mparment (MCI)), has attracted more and more attenton recently. So far, multple bomarkers have been shown to be senstve to the dagnoss of AD and MCI,.e., structural MR magng () for bran atrophy measurement, functonal magng (e.g., FDG-) for hypometabolsm quantfcaton, and cerebrospnal flud (CSF) for quantfcaton of specfc protens. However, most exstng research focuses on only a sngle modalty of bomarkers for dagnoss of AD and MCI, although recent studes have shown that dfferent bomarkers may provde complementary nformaton for the dagnoss of AD and MCI. In ths paper, we propose to combne three modaltes of bomarkers,.e.,, FDG-, and CSF bomarkers, to dscrmnate between AD (or MCI) and healthy controls, usng a kernel combnaton method. Specfcally, ADNI baselne, FDG-, and CSF data from 51 AD patents, 99 MCI patents (ncludng 43 MCI converters who had converted to AD wthn 18 months and 56 MCI non-converters who had not converted to AD wthn 18 months), and 52 healthy controls are used for development and valdaton of our proposed multmodal classfcaton method. In partcular, for each MR or FDG- mage, 93 volumetrc features are extracted from the 93 regons of nterest (ROIs), automatcally labeled by an atlas warpng algorthm. For CSF bomarkers, ther orgnal values are drectly used as features. Then, a lnear support vector machne (SVM) s adopted to evaluate the classfcaton accuracy, usng a 10-fold cross-valdaton. As a result, for classfyng AD from healthy controls, we acheve a classfcaton accuracy of 93.2% (wth a senstvty of 93% and a specfcty of 93.3%) when combnng all three modaltes of bomarkers, and only 86.5% when usng even the best ndvdual modalty of bomarkers. Smlarly, for classfyng MCI from healthy controls, we acheve a classfcaton accuracy of 76.4% (wth a senstvty of 81.8% and a specfcty of 66%) for our combned method, and only 72% even usng the best ndvdual modalty of bomarkers. Further analyss on MCI senstvty of our combned method ndcates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classfed. Moreover, we also evaluate the classfcaton performance when employng a feature selecton method to select the most dscrmnatve MR and FDG- features. Agan, our combned method shows consderably better performance, compared to the case of usng an ndvdual modalty of bomarkers Elsever Inc. All rghts reserved. Introducton Alzhemer's dsease (AD) s the most common form of dementa n elderly people worldwde. It s reported that the number of affected people s expected to double n the next 20 years, and 1 n 85 people wll be affected by 2050 (Ron et al., 2007). Thus, accurate dagnoss of AD, Correspondng author. Fax: E-mal addresses: dqzhang@nuaa.edu.cn (D. Zhang), dgshen@med.unc.edu (D. Shen). 1 Data used n preparaton of ths artcle were obtaned from the Alzhemer's Dsease Neuromagng Intatve (ADNI) database ( As such, the nvestgators wthn the ADNI contrbuted to the desgn and mplementaton of ADNI and/or provded data but dd not partcpate n analyss or wrtng of ths report. A complete lstng of ADNI nvestgators can be found at: Collaboraton\ADNI_Authorshp_lst.pdf. especally for ts early stage also known as amnestc mld cogntve mparment (MCI), s very mportant. It s known that AD s related to the structural atrophy, pathologcal amylod depostons, and metabolc alteratons n the bran (Jack et al., 2010; Nestor et al., 2004). At present, several modaltes of bomarkers have been proved to be senstve to AD and MCI, ncludng the bran atrophy measured n magnetc resonance (MR) magng (de Leon et al., 2007; Du et al., 2007; Fjell et al., 2010; McEvoy et al., 2009), hypometabolsm measured by functonal magng (De Sant et al., 2001; Morrs et al., 2001), and quantfcaton of specfc protens measured through CSF (Bouwman et al., 2007b; Fjell et al., 2010; Mattsson et al., 2009; Shaw et al., 2009). However, most exstng pattern classfcaton methods just use one ndvdual modalty of bomarkers for dagnoss of AD or MCI, whch may affect the overall classfcaton performance. For example, many hgh-dmensonal classfcaton methods use only the structural /$ see front matter 2011 Elsever Inc. All rghts reserved. do: /j.neuromage

2 D. Zhang et al. / NeuroImage 55 (2011) bran mages for classfcaton between AD (or MCI) and healthy controls (Cungnet et al., n press; Fan et al., 2008a, 2007; Gerardn et al., 2009; Kloppel et al., 2008; Lao et al., 2004; Magnn et al., 2009; Msra et al., 2009; Olvera et al., 2010; Westman et al., 2011). Also, accordng to the features beng extracted from the structural, the exstng classfcaton methods can be roughly dvded nto three categores, usng 1) voxel-wse tssue probablty (Fan et al., 2007; Kloppel et al., 2008; Lao et al., 2004; Magnn et al., 2009), 2) cortcal thckness (Deskan et al., 2009; Lerch et al., 2008; Olvera et al., 2010; Querbes et al., 2009), and 3) hppocampal volumes (Gerardn et al., 2009; West et al., 2004). It was found that most effectve features for AD or MCI classfcaton are actually extracted from the atrophc regons,.e., hppocampus, entorhnal cortex, parahppocampal gyrus, and cngulated, whch are consstent wth prevous fndngs usng group comparson methods (Chetelat et al., 2002; Convt et al., 2000; Fox and Schott, 2004; Jack et al., 1999; Msra et al., 2009). In addton to structural, another mportant modalty of bomarkers for AD or MCI detecton s fluorodeoxyglucose postron emsson tomography (FDG-) (Chetelat et al., 2003; Foster et al., 2007; Hgdon et al., 2004). Wth FDG-, some recent studes have reported the reducton of glucose metabolsm n paretal, posteror cngulated, and temporal bran regons for AD patents (Dehl et al., 2004; Drzezga et al., 2003). Besdes these neuromagng technques, there are also some bologcal or genetc bomarkers developed for dagnoss of AD or MCI. For example, researchers have found that 1) the ncreased CSF total tau (t-tau) and tau hyperphosphorylated at threonne 181 (p-tau) are related to the neurofbrllary tangle pathology, 2) the decreased amylod β (Aβ 42 ) ndcates amylod plaque pathology, and 3) the presence of the apolpoproten E (APOE) ε4 allele can predct cogntve declne or converson to AD (Bouwman et al., 2007b; de Leon et al., 2007; Fjell et al., 2010; J et al., 2001). Actually, dfferent bomarkers provde complementary nformaton, whch may be useful for dagnoss of AD or MCI when used together (Apostolova et al., 2010; de Leon et al., 2007; Fjell et al., 2010; Foster et al., 2007; Landau et al., 2010; Walhovd et al., 2010b). It was reported that FDG- and measures are dfferentally senstve to memory n health and dsease (Walhovd et al., 2010b). A recent study also shows that the morphometrc changes n AD and MCI are related to CSF bomarkers, but can also provde complementary nformaton to CSF bomarkers (Fjell et al., 2010). A more recent study has compared the respectve prognostc ablty of genetc, CSF, neuromagng, and cogntve measures obtaned n the same partcpants, ndcatng that there exsts complementary nformaton among these bomarkers whch may ad n the future dagnoss of AD and MCI (Landau et al., 2010). Inspred by these fndngs, a few studes have used two or more bomarkers smultaneously for detecton of AD and MCI,.e., usng and CSF n Bouwman et al. (2007a) and Vemur et al. (2009), and cogntve testng n Gerold et al. (2006), Vsser et al. (2002), FDG- and CSF n Fellgebel et al. (2007), FDG- and cogntve testng n Chetelat et al. (2005), and, CSF, and FDG- n Walhovd et al. (2010a). Although the use of multple bomarkers yelds promsng results, the above methods may be lmted. Frst, only a few manually selected bran regons are generally consdered for and based classfcaton of AD or MCI. However, the structural and functonal features measured from a lmted set of pre-defned regons may be not able to reflect the spatal temporal pattern of structural and physologcal abnormaltes n ther entrety (Fan et al., 2008b). Second, most of the above methods are prmarly desgned to characterze group dfferences, and are not for ndvdual classfcaton. Although there exst some methods combnng two modaltes of bomarkers for ndvdual classfcaton,.e., usng both and (Fan et al., 2008b; Hnrchs et al., 2009a, 2009b; Ye et al., 2008), both and CSF (Davatzkos et al., n press), or both and APOE bomarkers (Ye et al., 2008), there are stll a few methods that combne all three modaltes of bomarkers (,, and CSF) for classfcaton, and n ths paper the beneft of combnng all three bomarkers for AD or MCI dagnoss wll be shown. Specfcally, we wll combne the measurements from all three bomarkers,.e.,,, and CSF, to dscrmnate between AD and healthy controls, or between MCI and healthy controls. To effectvely combne three dfferent bomarkers for classfcaton, we use a smplewhle-effectve multple-kernel combnaton method. Ths method can be naturally embedded nto the conventonal SVM classfer wthout extra steps. Our expermental results show that the combnaton of dfferent measurements from,, and CSF demonstrates much better performance n AD or MCI classfcaton, compared to the case of usng even the best ndvdual modalty of bomarkers. Methods The data used n the preparaton of ths artcle were obtaned from the Alzhemer's Dsease Neuromagng Intatve (ADNI) database ( The ADNI was launched n 2003 by the Natonal Insttute on Agng (NIA), the Natonal Insttute of Bomedcal Imagng and Boengneerng (NIBIB), the Food and Drug Admnstraton (FDA), prvate pharmaceutcal companes and non-proft organzatons, as a $60 mllon, 5-year publc prvate partnershp. The prmary goal of ADNI has been to test whether seral,, other bologcal markers, and clncal and neuropsychologcal assessments can be combned to measure the progresson of MCI and early AD. Determnaton of senstve and specfc markers of very early AD progresson s ntended to ad researchers and clncans to develop new treatments and montor ther effectveness, as well as lessen the tme and cost of clncal trals. ADNI s the result of efforts of many convestgators from a broad range of academc nsttutons and prvate corporatons, and subjects have been recruted from over 50 stes across the U.S. and Canada. The ntal goal of ADNI was to recrut 800 adults, ages 55 to 90, to partcpate n the research approxmately 200 cogntvely normal older ndvduals to be followed for 3 years, 400 people wth MCI to be followed for 3 years, and 200 people wth early AD to be followed for 2 years (see for up-to-date nformaton). The research protocol was approved by each local nsttutonal revew board and wrtten nformed consent s obtaned from each partcpant. Subjects The ADNI general elgblty crtera are descrbed at Brefly, subjects are between years of age, havng a study partner able to provde an ndependent evaluaton of functonng. Specfc psychoactve medcatons wll be excluded. General ncluson/excluson crtera are as follows: 1) healthy subjects: Mn- Mental State Examnaton (MMSE) scores between 24 30, a Clncal Dementa Ratng (CDR) of 0, non-depressed, non-mci, and nondemented; 2) MCI subjects: MMSE scores between 24 30, a memory complant, havng objectve memory loss measured by educaton adjusted scores on Wechsler Memory Scale Logcal Memory II, a CDR of 0.5, absence of sgnfcant levels of mparment n other cogntve domans, essentally preserved actvtes of daly lvng, and an absence of dementa; and 3) mld AD: MMSE scores between 20 26, CDR of 0.5 or 1.0, and meets the Natonal Insttute of Neurologcal and Communcatve Dsorders and Stroke and the Alzhemer's Dsease and Related Dsorders Assocaton (NINCDS/ADRDA) crtera for probable AD. In ths paper, only ADNI subjects wth all correspondng, CSF and baselne data are ncluded. Ths yelds a total of 202 subjects ncludng 51 AD patents, 99 MCI patents (43 MCI converters who had converted to AD wthn 18 months and 56 MCI non-converters who had not converted to AD wthn 18 months), and 52 healthy controls.

3 858 D. Zhang et al. / NeuroImage 55 (2011) Table 1 lsts the demographcs of all these subjects. Subject IDs are gven n Supplemental Table 5. All structural MR scans used n ths paper were acqured from 1.5 T scanners. Data were collected across a varety of scanners wth protocols ndvdualzed for each scanner, as defned at edu/adni/research/cores/ndex.shtml. Brefly, raw Dgtal Imagng and Communcatons n Medcne (DICOM) scans were downloaded from the publc ADNI ste ( revewed for qualty, and automatcally corrected for spatal dstorton caused by gradent nonlnearty and B 1 feld nhomogenety. We downloaded the baselne data from the ADNI web ste ( n December A detaled descrpton of protocols and acquston can be found at Brefly, mages were acqured mn post-njecton, averaged, spatally algned, nterpolated to a standard voxel sze, ntensty normalzed, and smoothed to a common resoluton of 8-mm full wdth at half maxmum. CSF We downloaded the baselne CSF Aβ 42, t-tau and p-tau data from the ADNI web ste ( n December The CSF collecton and transportaton protocols are provded n the ADNI procedural manual on Brefly, CSF was collected n the mornng after an overnght fast usng a 20- or 24-gauge spnal needle, frozen wthn 1 hour of collecton, and transported on dry ce to the ADNI Bomarker Core laboratory at the Unversty of Pennsylvana Medcal Center. In ths study, CSF Aβ 42, CSF t-tau and CSF p-tau are used as the features. Image analyss Table 1 Subject nformaton. AD (n=51; 18F/33M) MCI (n=99; 32F/67M) HC (n=52; 18 F/34 M) Mean SD Range Mean SD Range Mean SD Range Age Educaton MMSE CDR The numbers refer to baselne data. AD=Alzhemer's Dsease, MCI=Mld Cogntve Imparment, HC=Healthy Control, MMSE=Mn-Mental State Examnaton, CDR= Clncal Dementa Ratng. Image pre-processng s performed for all MR and mages. Frst, we do anteror commssure (AC) posteror commssure (PC) correcton on all mages, and use the N3 algorthm (Sled et al., 1998) to correct the ntensty nhomogenety. Next, we do skull-strppng on structural MR mages usng both bran surface extractor (BSE) (Shattuck et al., 2001) and bran extracton tool (BET) (Smth, 2002), followed by manual edton and ntensty nhomogenety correcton. After removal of cerebellum, FAST n the FSL package (Zhang et al., 2001) s used to segment structural MR mages nto three dfferent tssues: gray matter (GM), whte matter (WM), and cerebrospnal flud (CSF). After regstraton usng HAMMER (Shen and Davatzkos, 2002), we obtan the subject-labeled mage based on a template wth 93 manually labeled ROIs (Kaban et al., 1998). For each of the 93 ROI regons n the labeled MR mage, we compute the volume of GM tssue n that ROI regon as a feature. For mage, we frst algn t to ts respectve MR mage of the same subject usng a rgd transformaton, and then compute the average ntensty of each ROI regon n the mage as a feature. Therefore, for each subject, we totally obtan 93 features from the mage, another 93 features from the mage, and 3 features from the CSF bomarkers. Multmodal data fuson and classfcaton A general framework based on kernel methods (Scholkopf and Smola, 2002) s presented here to combne multple bomarkers (,, and CSF) for dscrmnatng between AD (or MCI) and healthy controls. Ths kernel-based method can be easly embedded nto the conventonal SVM classfer for hgh-dmensonal pattern classfcaton, wthout extra steps. Moreover, unlke other combnng methods whch can only process one type of data,.e., numerc data type, our method can combne multple types of data such as numerc data, strng, and graph. Before ntroducng the kernel combnaton method, we frst brefly revew the standard sngle-kernel SVM algorthm. The man dea of SVM s summarzed as follows. Frst, the lnearly nonseparable samples are mapped from ther orgnal space to a hgher or even nfnte dmensonal feature space, where they are more lkely to be lnearly separable than n the orgnal lower-dmensonal space, through a kernel-nduced mplct mappng functon. Then, a maxmum margn hyperplane s sought n the hgher-dmensonal space. Now we wll present the multple-kernel SVM whch can be used to ntegrate multple modaltes of bomarkers (.e.,, and CSF) for ndvdual classfcaton of AD (or MCI) from healthy controls. Suppose that we are gven n tranng samples and each of them s of M modaltes. Let x (m) denote a feature vector of the m-th modalty of the -th sample, and ts correspondng class label be y {1, 1}. Multplekernel based SVM solves the followng prmal problem: 1 M mn w ðmþ ;b;ξ 2 β m w ðmþ 2 n + C m =1 =1 M s:t: y m =1 β m ξ 0; =1; ; n: w ðmþ Tϕ ðm Þ ξ! + b 1 ξ Where w ðmþ, ϕ (m) and β m 0 denote the normal vector of hyperplane, the kernel-nduced mappng functon, and the combnng weght on the m-th modalty, respectvely. Smlarly as n the conventonal SVM, the dual form of multplekernel SVM can be represented as below: max α n α 1 =1 2 ;j s:t: n α y =0 =1 0 α C; =1; ; n: M α α j y y j β m k m m =1 ð Þ ; Tϕ Where k ðmþ ; j = ϕ ðmþ ðmþ j s the kernel functon for the two tranng samples on the m-th modalty. The symbol n s the number of tranng samples. For a new test sample x = x ð1þ ; x ð2þ ; ; x ðmþ,wefrst denote Tϕ k ðmþ ; = ϕ ðmþ ðm Þ as the kernel between the new test sample and each tranng sample on the m-th modalty. Then, the decson functon for the predcted label can be obtaned as below: f x ð1þ ; x ð2þ ; ; x ðmþ n = sgn =1 y α M m =1 β m k m j ð Þ! ; + b :

4 D. Zhang et al. / NeuroImage 55 (2011) It's easy to know that the multple-kernel based SVM can be naturally embedded nto the conventonal sngle-kernel SVM f we nterpret k x ; x j = m β m k ðmþ ; j as a mxed kernel between themultmodal tranng samples x and x j, and kðx ; xþ = m β m k ðmþ ; as a mxed kernel between the multmodal tranng sample x and the test sample x. In fact, our method can be vewed as a way for a kernel combnaton whch combnes multple kernels nto one kernel. It s worth notng that our formulaton of multple-kernel SVM s smlar, but dfferent from, the exstng mult-kernel learnng methods (Hnrchs et al., 2009b; Lanckret et al., 2004; Wang et al., 2008). One key dfference s that we do not jontly optmze the weghts β m s together wth other SVM parameters (e.g., α) n an teratve way. Instead, we constran m β m = 1 and use a coarse-grd search through cross-valdaton on the tranng samples to fnd the optmal values. After we obtan the values of β m s, we use them to combne multple kernels nto a mxed kernel, and then perform the standard SVM usng the mxed kernel. The man advantage of our method s that t can be convenently solved usng the conventonal SVM solvers, e.g., LIBSVM (Chang and Ln, 2001). As explaned above, ths kernel combnaton method can provde a convenent and effectve way for fusng varous data from dfferent modaltes. In our case, we focus on multmodal classfcaton usng three modaltes,.e.,,, and CSF bomarkers. Fg. 1 gves a schematc llustraton of our multmodal data fuson and classfcaton ppelne. Valdaton To evaluate the performance of dfferent classfcaton methods, we use a 10-fold cross-valdaton strategy to compute the classfcaton accuracy (for measurng the proporton of subjects correctly classfed among the whole populaton), as well as the senstvty (.e., the proporton of AD or MCI patents correctly classfed) and the specfcty (.e., the proporton of healthy controls correctly classfed). Specfcally, the whole set of subject samples are equally parttoned nto 10 subsets, and each tme the subject samples wthn one subset are successvely selected as the testng samples and all remanng subject samples n the other 9 subsets are used for tranng the multple-kernel classfer. Ths process s repeated for 10 tmes ndependently to avod any bas ntroduced by randomly parttonng dataset n the cross-valdaton. The SVM classfer s mplemented usng LIBSVM toolbox (Chang and Ln, 2001), wth a lnear kernel and a default value for the parameter C (.e., C =1). The weghts n the multple-kernel classfcaton method are learned based on the Fg. 1. Schematc llustraton of multmodal data fuson and classfcaton ppelne.

5 860 D. Zhang et al. / NeuroImage 55 (2011) tranng samples, through a grd search usng the range from 0 to 1 at a step sze of 0.1. Specfcally, n each fold of the 10-fold crossvaldaton, we perform another 10-fold cross-valdaton on the tranng samples to determne the optmal values for the weghts. Also, for each feature f n the tranng samples, a common feature normalzaton scheme s adopted,.e., f = f f = σ, where f and σ are respectvely the mean and standard devaton of the -th feature across all tranng samples. The estmated f and σ wll be used to normalze the correspondng feature of each test sample. Results Multmodal classfcaton based on,, and CSF We frst test the performance of our multmodal classfcaton method n dentfcaton of AD (or MCI) from healthy controls, based on,, and CSF bomarkers of 202 baselne subjects n ADNI. Table 2 shows the classfcaton rate of our multmodal classfcaton method, compared wth the methods usng each ndvdual modalty only. Note that Table 2 shows only the averaged results of 10 ndependent experments, along wth the mnmal and maxmal values gven n brackets; and the detaled results can be found n the Supplemental Fgs. 8, 9 for each experment. Besdes, Fg. 2 further plots the correspondng ROC curves of dfferent classfcaton methods for AD or MCI, respectvely. As we can see from Table 2 and Fg. 2, the combned measurements of,, and CSF consstently acheve more accurate dscrmnaton between AD (or MCI) patents and healthy controls. Specfcally, for classfyng AD from healthy controls, our multmodal classfcaton method can acheve a classfcaton accuracy of 93.2%, a senstvty of 93%, and a specfcty of 93.3%, whle the best accuracy on ndvdual modalty s only 86.5% (when usng ). On the other hand, for classfyng MCI from healthy controls, our multmodal classfcaton method acheve a classfcaton accuracy of 76.4%, a senstvty of 81.8%, and a specfcty of 66%, whle the best accuracy on ndvdual modalty s only 72% (when usng ). In addton, the area under the ROC curve (AUC) s and for AD classfcaton and MCI classfcaton respectvely wth our multmodal classfcaton method (see Fg. 2), whle the best AUC on ndvdual modalty s (when usng ) for AD classfcaton and (when usng ) for MCI classfcaton. Table 2 also ndcates that, for AD classfcaton, there are lttle dfferences among accuracy, senstvty, and specfcty of each classfcaton method (a total of 5 methods examned), whle for MCI classfcaton the dfferences s relatvely large, e.g., a relatvely large senstvty, but low specfcty, for each method. Ths characterstc of possessng hgh senstvty may be advantageous for the purpose of dagnoss, because the cost s dfferent for msclassfyng an MCI patent nto a healthy control (wth senstvty reduced n ths case) and msclassfyng a healthy control nto an MCI patent (wth specfcty reduced n ths case), and the former cost s much hgher than the latter. Inspred from ths observaton, we further dvde the MCI cohort nto MCI converters who converted to AD wthn 18 months and the MCI non-converters who had not convert to AD wthn 18 months, and then compute how many MCI converters and MCI non-converters are correctly classfed as MCI. The results wth our multmodal classfcaton method reveal that the 91.5% MCI converters and 73.4% MCI nonconverters are correctly classfed. It's worth notng that n practce the cost of msclassfyng MCI converters s usually much hgher than that of msclassfyng MCI non-converters. Thus, ths characterstc of possessng a hgher classfcaton rate for the MCI converters by our method s potentally very useful. For comparson wth other multmodal classfcaton methods, we also perform the use of drect feature concatenaton as a baselne method for multmodal AD (or MCI) classfcaton. Specfcally, for each subject, we frst concatenate 93 features from, 93 features from, and 3 features from CSF, nto a 189 dmensonal vector. Remember that each feature has been normalzed to have zero mean and unt standard devaton. Then, we perform SVM-based classfcaton on all samples wth a 10-fold cross-valdaton strategy as descrbed above, and obtan the classfcaton results n the bottom row of Table 2. As we can observe from Table 2, our kernel combnaton method consstently outperforms the baselne method on each performance measure. Furthermore, n Table 3 we compared the proposed method wth a recent method proposed n Hnrchs et al. (n press). The latter used 114 ADNI subjects (48 AD +66HC) for AD classfcaton, and t reported both results of usng only magng modaltes (+) and all modaltes (++CSF+APOE+Cogntve scores), as ncluded n Table 3. The proposed method uses a smlar number of ADNI subjects,.e., 103 subjects (51 AD+52HC), wth results gven n Table 2. For comparson, we also nclude the proposed method's results n Table 3. As we can observe from Table 3, the proposed method s superor to Hnrchs et al.'s method n case of usng only magng modalty (+) or all modaltes (++CSF). It's worth notng that, n Hnrchs et al. (n press), both baselne and longtudnal data are used for and modaltes, whle the proposed method uses only the baselne data. In the second case, even f the addtonal APOE and cogntve scores were used n Hnrchs et al.'s method, our result s stll better. These results further valdate the effcacy of the proposed method for multmodal classfcaton. Comparson of dfferent combnaton schemes To nvestgate the effect of dfferent combnng weghts,.e., β, β CSF, and β, on the performance of our multmodal classfcaton method, we test all of ther possble values, rangng from 0 to 1 at a step sze of 0.1, under the constrant of β +β CSF +β =1. Fgs. 3 and 4 show the classfcaton results, ncludng accuracy (top row), Table 2 Comparson of performance of sngle-modal and multmodal classfcaton methods. The numbers n each bracket denote the mnmal and maxmal classfcaton rate n 10 ndependent experments. Methods AD vs. HC MCI vs. HC ACC (%) SEN (%) SPE (%) ACC (%) SEN (%) SPE (%) ( ) ( ) ( ) ( ) ( ) ( ) CSF ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Combned ( ) ( ) ( ) ( ) ( ) ( ) Baselne ( ) ( ) ( ) ( ) ( ) ( ) AD=Alzhemer's Dsease, MCI=Mld Cogntve Imparment, HC=Healthy Control, ACC=classfcaton ACCuracy, SEN=SENstvty, SPE=SPEcfcty.

6 D. Zhang et al. / NeuroImage 55 (2011) True postve rate (TPR) True postve rate (TPR) ROC for AD vs. HC classfcaton Combned CSF False postve rate (FPR) ROC for MCI vs. HC classfcaton Combned CSF False postve rate (FPR) Fg. 2. ROC curves of dfferent methods, for AD classfcaton (top) and for MCI classfcaton (bottom). senstvty (bottom left), and specfcty (bottom rght), wth respect to dfferent combnng weghts of,, and CSF. Note that, n each subplot, only the squares n the upper trangular part have vald values because of the constrant β +β CSF +β =1. For each plot, the three vertces of the upper trangle,.e., the top left, top rght, and bottom left squares, denote ndvdual-modalty based classfcaton results usng only (β =1), CSF (β CSF =1), and (β =1), respectvely. As we can observe from Fgs. 3 and 4, nearly all nner squares of the upper trangle have larger values (better classfcaton) than the three vertces, whch demonstrates the effectveness of combnng three modaltes n AD (or MCI) classfcaton. Moreover, for most plots, there are substantally a large set of squares ownng hgher classfcaton accuracy. Further observaton ndcates that the squares wth hgher accuracy manly appear n the nner squares of each trangle, nstead of the boundary, mplyng that each modalty s ndspensable for achevng good classfcaton. Smlar to what we have observed from Table 2, Fgs. 3 and 4 also show that, for AD classfcaton, the dfferences among accuracy, senstvty, and specfcty are small, whle, for MCI classfcaton, t tends to have a hgher senstvty but lower specfcty. Classfcaton performance wth respect to the number of selected ROI features We have shown the effectveness of our multple-kernel combnaton method on usng whole-bran ROI features (wthout feature selecton) for AD or MCI classfcaton. Here, we nvestgate how the performance of our multple-kernel combnaton method changes wth respect to the number of the selected ROI features. To ths end, we frst use a pared t-test, respectvely, on and data of tranng samples to choose the most dscrmnatve bran regons or features for gudng AD or MCI classfcaton (Gerardn et al., 2009). It's worth notng that the feature selecton s performed usng only the tranng samples, nstead of all samples. Specfcally, n each fold of the 10-fold cross-valdatons, we perform a t-test only on the tranng samples to select the most dscrmnatve feature subset. Table 4 lsts the top bran regons (or ROIs) detected from both and data n MCI classfcaton, and Fgs. 5 and 6 show these top bran regons n the template space. Totally, 11 top bran regons, wth correspondng p-values less than 0.002, are determned n mages. Notce that the top regons selected for AD classfcaton are not lsted, snce the number s too large. As shown n Table 4 and Fgs. 5 and 6, most of the selected top regons, e.g., hppocampal, amygdale, entorhnal cortex, uncus, temporal pole and parahppocampal regons, are known to be related to the AD by many studes usng group comparson methods (Chetelat et al., 2002; Convt et al., 2000; Fox and Schott, 2004; Jack et al., 1999; Msra et al., 2009). For example, the hppocampus s a structure hghly related to the memory, whch s always affected n the AD. Then, we test the classfcaton performances of dfferent methods wth respect to the dfferent number of bran regons selected for AD (or MCI) classfcaton, wth results shown n Fg. 7. As we can see from Fg. 7, for both AD classfcaton and MCI classfcaton, our multmodal classfcaton method (usng all,, and CSF) acheves consstent mprovement over those usng only one ndvdual modalty, for any number of bran regons selected. Moreover, compared wth ndvdual-modalty based methods, our multmodal classfcaton method s more robust to the number of bran regons used for classfcaton. For example, Fg. 7 shows that, even only one bran regon s selected for and mages, our multmodal classfcaton method can stll acheve a reasonable classfcaton accuracy, compared to the ndvdual-modalty based classfcaton methods. Another nterestng observaton from Fg. 7 s that more bran regons are needed for achevng hgher accuracy for MCI classfcaton than AD classfcaton. Ths ndcates that, wth the progress of dsease, more atrophes are produced n AD, thus a small number of bran regons wth relatvely large atrophes s suffcent for successful classfcaton of AD. Table 3 Comparson of performance of dfferent multmodal classfcaton methods. Methods Subjects Modaltes ACC (%) SEN (%) SPE (%) Hnrchs et al., n press 48 AD+66 HC CSF+APOE+cogntve scores Proposed method 51 AD+52 HC CSF AD=Alzhemer's Dsease, HC=healthy control, ACC=classfcaton ACCuracy, SEN=SENstvty, SPE=SPEcfcty.

7 862 D. Zhang et al. / NeuroImage 55 (2011) Fg. 3. AD classfcaton results wth respect to dfferent combnng weghts of, and CSF. Dscusson In ths paper, we have proposed a new multmodal data fuson and classfcaton method to automatcally dscrmnate patents wth AD (or MCI) from healthy controls, usng a kernel combnaton method. Ths kernel combnaton method can be naturally embedded nto the conventonal SVM and solved effcently. The results on 202 baselne subjects from ADNI show that our multmodal classfcaton method can consstently and substantally mprove the classfcaton performance of the ndvdual-modalty based classfcaton methods. Specfcally, our method can acheve a hgh accuracy (93.2%) for AD classfcaton, a relatvely hgh senstvty (81.8%) for MCI classfcaton, and especally a hgh senstvty (91.5%) for classfcaton of MCI converters. Multmodal data fuson and classfcaton A lot of studes have shown that bomarkers from dfferent modaltes may contan complementary nformaton for dagnoss of AD (Apostolova et al., 2010; de Leon et al., 2007; Fjell et al., 2010; Foster et al., 2007; Landau et al., 2010; Walhovd et al., 2010b). Recently, several works on combnng dfferent modaltes of bomarkers have been reported (Bouwman et al., 2007a; Chetelat et al., 2005; Fan et al., 2008b; Fellgebel et al., 2007; Gerold et al., 2006; Vemur et al., 2009; Vsser et al., 2002; Walhovd et al., 2010a). A common practce n these works s the concatenaton of all features (from dfferent modaltes) nto a longer feature vector. However, ths may be not enough for effectve combnaton of features from dfferent modaltes. In ths paper, we provde an alternatve way by usng a kernel combnaton to ntegrate dfferent bomarkers. Compared wth the drect feature concatenaton method, the kernel combnaton method has the followng advantages: 1) t provdes a unfed way to combne heterogeneous data when a dfferent type of data cannot be drectly concatenated; and 2) t offers more flexblty by usng dfferent weghts on bomarkers of dfferent modaltes. For nstance, we cannot drectly concatenate data represented by strngs or graphs wth numerc data whle we can possbly construct separate kernels for strng, graphs and numerc data respectvely and then fuse them by kernel combnaton. In our case, snce,, and CSF are dfferent types of features, the kernel combnaton provdes us a better way to ntegrate them for gudng the classfcaton. It's worth notng that the kernel combnaton method has been successfully appled to many other felds,.e., proten functon predcton (Lanckret et al., 2004), cancer dagnoss (Yu et al., 2010), and gene prortzaton (De Be et al., 2007). Recently, several researches have started to use ths powerful kernel combnaton method for AD study (Hnrchs et al., 2009b; Ye et al., 2008). Specfcally, n Ye et al. (2008), and APOE data as well as the age and sex nformaton were combned usng the exstng multplekernel learnng method. In Hnrchs et al. (2009b), and data were combned also usng the same multple-kernel learnng method. However, both studes amed only for AD classfcaton, whle n ths paper we studed for both AD classfcaton and MCI classfcaton. The latter s actually more mportant than the former for early detecton and treatment of AD. More mportantly, we combne not only and, but also CSF, whch was rarely nvestgated before n the multple-kernel combnaton study. Our expermental result shows that each modalty (,, and CSF) s ndspensable for achevng good combnaton and classfcaton. Also, we use a more advanced feature extracton method wth atlas warpng, compared to

8 D. Zhang et al. / NeuroImage 55 (2011) Fg. 4. MCI classfcaton results wth respect to dfferent combnng weghts of, and CSF. those n Hnrchs et al. (2009b) and Ye et al. (2008). Thus, we can acheve much better performance compared to those reported n Hnrchs et al. (2009b) and Ye et al. (2008). Even for ther new method usng baselne,, CSF, and addtonal longtudnal and data, bologcal measures, and cogntve scores (Hnrchs Table 4 Top 11 bran regons selected for MCI classfcaton detected from and modaltes (ranked accordng to the p-values n the brackets). 1 Amygdala rght Angular gyrus left (pb0.0001) (p=0.0003) 2 Hppocampal formaton left Precuneus left (pb0.0001) (p=0.0005) 3 Hppocampal formaton rght Precuneus rght (pb0.0001) (p=0.0021) 4 Uncus left Inferor temporal gyrus left (pb0.0001) (p=0.0146) 5 Entorhnal cortex left Anteror lmb of nternal capsule rght (p=0.0001) (p=0.0154) 6 Amygdala left Angular gyrus rght (p=0.0001) (p=0.0189) 7 Mddle temporal gyrus left Anteror lmb of nternal capsule left (p=0.0001) (p=0.0204) 8 Temporal pole left Globus palladus left (p=0.0004) (p=0.021) 9 Perrhnal cortex left Globus palladus rght (p=0.0004) (p=0.0259) 10 Uncus rght Posteror lmb of nternal capsule rght (p=0.0006) (p=0.0272) 11 Parahppocampal gyrus left Entorhnal cortex left (p=0.0009) (p=0.0286) et al., n press), ts performance s stll nferor to our method usng only baselne, and CSF, as shown n Table 3. Dversty of ndvdual modaltes n classfcaton As mentoned earler, a lot of studes have ndcated that dfferent modaltes contan complementary nformaton for dscrmnaton. Here, we quanttatvely measure the dscrmnaton smlarty and dversty between any two dfferent modaltes,.e., vs. CSF, vs., and CSF vs., by comparng ther ndvdual classfcaton results. Both Jaccard smlarty coeffcent and Kappa ndex are used to measure the smlartes and dverstes, respectvely. Small values on both ndexes mply a low smlarty and a hgh dversty on the two modaltes. For AD classfcaton, the averaged smlartes (dverstes) over 10-fold cross-valdaton are 0.75 (0.53), 0.80 (0.62), and 0.74 (0.49) for vs. CSF, vs., and CSF vs., respectvely. On the other hand, for MCI classfcaton, the averaged smlartes (dverstes) are 0.65 (0.33), 0.67 (0.38), and 0.63 (0.28), respectvely. These results ndcate that CSF and have the hghest complementary nformaton, whle and have the hghest smlar nformaton for classfcaton. Data fuson vs. ensemble In ths paper, we combne data from dfferent modaltes usng kernel combnaton, whch frst combnes multple kernel matrces from dfferent modaltes nto a sngle kernel matrx and then trans a sngle SVM model from the combned kernel matrx. Interestngly, we can also combne results from multple modaltes at classfcaton stage. That s, we frst tran multple SVM models on multple kernel

9 864 D. Zhang et al. / NeuroImage 55 (2011) Fg. 5. Top 11 bran regons selected for MCI classfcaton detected from. matrces from dfferent modaltes. Then, for a new testng sample, each of these models wll have a predcaton on t, and fnally we aggregate all predctons to get the fnal decson on the new testng sample. Ths technque s also called ensemble learnng, whch has been a very popular learnng method for decades n the machne learnng communty (Tan and Glbert, 2003). We have compared our kernel combnaton method wth the ensemble learnng method for AD (or MCI) classfcaton. Specfcally, the ensemble learnng method trans 3 SVM classfers from,, and CSF, respectvely; and then the majorty votng s used to get the fnal class labels for each new testng sample. The ensemble learnng method obtans a classfcaton accuracy of 91.8% for AD classfcaton, and an accuracy of 75.6% for MCI classfcaton, whch are slghtly nferor to the correspondng classfcaton numbers acheved by our kernel combnaton method. These results ndcate the effectveness of the ensemble learnng method as a useful and general way n mprovng classfcaton accuracy of ndvdual modaltes. It may be even more nterestng to nvestgate addng the mxed kernel from the kernel combnaton nto the ensemble or just ensemblng dfferent mxed kernels wth dfferent weghts. However, the full nvestgaton on ths topc s beyond the focus of ths paper. On the other hand, t s worth notng the dsadvantage of the ensemble learnng,.e., the dffculty n nterpretng the model snce multple models are used n the ensemble learnng. Ths ssue may lmt ts use n some medcal applcatons where n addton to the accuracy, nterpretablty s also concerned and mportant. Effect of feature selecton We test the kernel combnaton method on two cases,.e. wthout and wth feature selecton. It s worth notng that the man concern of usng feature selecton n the current study s to valdate the effectveness of the kernel combnaton on the selected bran regons. Therefore, we adopt a smple feature selecton method based on t-test statstcs, whch has been wdely used n the neuromagng analyss. Fg. 7 shows that even a smple feature selecton method can potentally select effectve features (or regons) for achevng hgher classfcaton accuracy than the orgnal methods usng all features. We expect that the use of more advanced feature selecton methods n the future can lead to further mprovement for our multmodal classfcaton. On the other hand, n the current study we adopt a lnear SVM as the classfer, whch ntrnscally uses a feature weghtng mechansm,.e., the absolute values of components n the normal vector of SVM's hyperplane can be regarded as weghts on features (Kloppel et al., 2008). In ths way, we can rank the features accordng to ther averaged SVM weghts. We fnd that the top-ranked features are partally dentcal wth those top features obtaned from a separate

10 D. Zhang et al. / NeuroImage 55 (2011) Fg. 6. Top 11 bran regons selected for MCI classfcaton detected from. feature selecton method we used. For example, among the topranked eleven features selected (accordng to SVM weghts) for MCI classfcaton on modalty, sx features, namely, amygdala rght, hppocampal formaton left, hppocampal formaton rght, entorhnal cortex left, temporal pole left, and parahppocampal gyrus left, are dentcal to those selected by the t-test statstcs as shown n Table 4. Notce that these sx bran regons are known to be related to AD and MCI by many studes n the lterature (Chetelat et al., 2002; Convt et al., 2000; Fox and Schott, 2004; Jack et al., 1999; Msra et al., 2009). Lmtatons Whle amng to develop a multmodal dagnostc tool, the current study s lmted by at least two factors. Frst, besdes,, and CSF, there are also other modaltes of data,.e., APOE. However, snce not every subject has data on all modaltes and the number of subjects wth all modaltes avalable s too small for reasonable classfcaton, the current study does not consder APOE for multmodal classfcaton. Second, n the current study, we nvestgate only the classfcaton between one stage of dementa (ether MCI or AD) and healthy controls, and do not test the ablty of the classfer to smultaneously dscrmnate multple stages of dementa,.e., mult-class classfcaton of AD, MCI, and healthy controls. Although the converson from bnary-class classfcaton to mult-class classfcaton seems straghtforward, wth many mult-class classfcaton methods avalable (Duda et al., 2001), there may be some problem and ths wll be our future work. Concluson Ths study proposes a new multmodal data fuson and classfcaton method based on kernel combnaton for AD and MCI. Compared wth the conventonal drect feature concatenaton method, our method provdes a unfed way to combne heterogeneous data, partcularly for the case where dfferent types of data cannot be drectly concatenated. Moreover, our method offers more flexblty by usng dfferent weghts for dfferent data modaltes. The results on 202 baselne subjects of ADNI show that our multmodal classfcaton method acheves a hgh accuracy for AD classfcaton and an encouragng accuracy for MCI classfcaton. The current study only consders the baselne data of the subjects n ADNI. In the future, we wll use both baselne and longtudnal data to predct the converson from MCI to AD by fndng the spatotemporal pattern of bran atrophy n multple modaltes. Moreover, we wll nvolve usng more modaltes of data (.e., APOE) nto our current multmodal classfcaton method. To overcome the lmtaton of the possble small number of subjects avalable for tranng and testng

11 866 D. Zhang et al. / NeuroImage 55 (2011) Classfcaton accuracy (%) Classfcaton accuracy (%) Number of regons classfer as dscussed earler, we wll seek more advanced methods n machne learnng whch can use mssng data for classfcaton,.e., sem-supervsed classfcaton. We expect that, by usng more samples (wth both complete and mssng modalty nformaton), the semsupervsed method wll mprove the classfcaton performance further. Acknowledgments AD vs. HC MCI vs. HC Combned CSF Combned CSF Number of regons Fg. 7. Classfcaton accuracy of four dfferent methods, wth respect to dfferent number of regons selected for AD classfcaton (top) and MCI classfcaton (bottom). Ths work was supported n part by NIH grants EB006733, EB008374, EB and MH Data collecton and sharng for ths project was funded by the Alzhemer's Dsease Neuromagng Intatve (ADNI) (Natonal Insttutes of Health Grant U01 AG024904). ADNI s funded by the Natonal Insttute on Agng, the Natonal Insttute of Bomedcal Imagng and Boengneerng, and through generous contrbutons from the followng: Abbott, AstraZeneca AB, Bayer Scherng Pharma AG, Brstol-Myers Squbb, Esa Global Clncal Development, Elan Corporaton, Genentech, GE Healthcare, GlaxoSmthKlne, Innogenetcs, Johnson and Johnson, El Llly and Co., Medpace, Inc., Merck and Co., Inc., Novarts AG, Pfzer Inc., F. Hoffman-La Roche, Scherng-Plough, Synarc, Inc., as well as non-proft partners the Alzhemer's Assocaton and Alzhemer's Drug Dscovery Foundaton, wth partcpaton from the U.S. Food and Drug Admnstraton. Prvate sector contrbutons to ADNI are facltated by the Foundaton for the Natonal Insttutes of Health ( The grantee organzaton s the Northern Calforna Insttute for Research and Educaton, and the study s coordnated by the Alzhemer's Dsease Cooperatve Study at the Unversty of Calforna, San Dego. 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