Psychiatry Research: Neuroimaging 233 (2015) 131–140

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Combination of dynamic 11C-PIB PET and structural MRI improves diagnosis of Alzheimer’s disease Linwen Liu a,b,1, Liping Fu c,1, Xi Zhang d,1, Jinming Zhang c, Xiaojun Zhang c,1, Baixuan Xu c, Jiahe Tian c, Yong Fan a,b,n a

Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China c Department of Nuclear Medicine, the Chinese People’s Liberation Army General Hospital, Beijing 100853, China d Department of Geriatric Neurology, the Chinese People’s Liberation Army General Hospital, Beijing 100853, China b

art ic l e i nf o

a b s t r a c t

Article history: Received 7 June 2014 Received in revised form 12 February 2015 Accepted 23 May 2015 Available online 30 May 2015

Structural magnetic resonance imaging (sMRI) is an established technique for measuring brain atrophy, and dynamic positron emission tomography with 11C-Pittsburgh compound B (11C-PIB PET) has the potential to provide both perfusion and amyloid deposition information. It remains unclear, however, how to better combine perfusion, amyloid deposition and morphological information extracted from dynamic 11C-PIB PET and sMRI with the goal of improving the diagnosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). We adopted a linear sparse support vector machine to build classifiers for distinguishing AD and MCI subjects from cognitively normal (CN) subjects based on different combinations of regional measures extracted from imaging data, including perfusion and amyloid deposition information extracted from early and late frames of 11C-PIB separately, and gray matter volumetric information extracted from sMRI data. The experimental results demonstrated that the classifier built upon the combination of imaging measures extracted from early and late frames of 11C-PIB as well as sMRI achieved the highest classification accuracy in both classification studies of AD (100%) and MCI (85%), indicating that multimodality information could aid in the diagnosis of AD and MCI. & 2015 Elsevier Ireland Ltd. All rights reserved.

Keywords: PET imaging Structural MRI Perfusion Amyloid Alzheimer’s disease

1. Introduction Structural magnetic resonance imaging (sMRI), positron emission tomography (PET), and other imaging modalities have been investigated as surrogate biomarkers of Alzheimer’s disease (AD). Structural MRI studies have identified structural changes associated with AD pathology, particularly in the medial temporal lobe (de Leon et al., 2007; Frisoni et al., 2013). PET imaging with 15 O-water, 18F-fluorodeoxyglucose (18F-FDG), and 11C-Pittsburgh compound B (11C-PIB) has been widely applied in AD studies for investigating regional cerebral blood flow (rCBF), regional cerebral metabolism, and amyloid-β (Aβ) deposition (Klunk et al., 2004; Foster et al., 2007; Paulson et al., 2010). Particularly, 18F-FDG has been used in neuroimaging studies for investigating synaptic dysfunction and neurodegeneration of AD (Jagust et al., 2007). It has been reported that under both physiological and pathological n Correspondence to: National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China. Fax.: þ 86 10 82544523. E-mail address: [email protected] (Y. Fan). 1 Linwen Liu, Liping Fu and Xi Zhang contributed equally to this work.

http://dx.doi.org/10.1016/j.pscychresns.2015.05.014 0925-4927/& 2015 Elsevier Ireland Ltd. All rights reserved.

situations, rCBF is coupled with cerebral metabolic rates of glucose (CMRglc) measured by FDG-PET (Nihashi et al., 2007). Although many studies have demonstrated that CMRglc is correlated with dementia severity and AD progression, CMRglc reductions might be caused by other disorders (Mosconi et al., 2010a; Kadir et al., 2012). As a PET tracer with an amyloid target, 11C-PIB has been demonstrated to bind specifically to extracellular and vascular fibrillar Aβ deposits in AD brains (Klunk et al., 2003; Ikonomovic et al., 2008; Rabinovici and Jagust, 2009). However, positive PIB results are found not only in AD (Klunk et al., 2004), but also in Lewy body dementia (Gomperts et al., 2008) and Parkinson’s -related dementia (Buongiorno et al., 2011). Therefore, it might improve AD diagnosis to combine amyloid pathological and CMRglc/rCBF information. From a dynamic 11C-PIB scan, besides PIB retention, which is typically measured using standardized uptake value ratio (SUVR, summed tissue uptake over an period) (Lopresti et al., 2005; Jack et al., 2008) or distribution volume ratio (DVR) (Innis et al., 2007), we can also use a sum of its early frames (perfusion PIB, 11C-pPIB) to estimate perfusion data that resemble measures of 18F-FDG

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(Rostomian et al., 2011; Forsberg et al., 2012; Fu et al., 2014). Therefore, a dynamic scan of 11C-PIB PET potentially provides complementary information including perfusion data (11C-pPIB) computed from early time frames and PIB retention estimated using DVR or summed SUVR (amyloid PIB, 11C-aPIB) from late frames. It remains difficult to obtain perfectly accurate clinical diagnosis of AD and its related dementia, even with the evidence of biomarkers, such as PET-amyloid imaging (Knopman et al., 2001; Dubois et al., 2007; Bouwman et al., 2010; Sperling and Johnson, 2010; Mosconi and McHugh, 2011a). Positive PIB results are found not only in AD patients (Rabinovici et al., 2007), but also in Lewy body dementia (Gomperts et al., 2008) and Parkinson’s diseaserelated dementia (Buongiorno et al., 2011), also in cognitively healthy elderly people (up to 30%) (Rowe et al., 2007; Morris et al., 2010). Several AD studies demonstrated that improved diagnostic performance could be achieved if PET measures were combined with sMRI or cerebrospinal fluid (CSF) information (Walhovd et al., 2010; Fan, 2011; Westman et al., 2012). It has also been demonstrated that PIB retention estimated from 40 to 60 min post-injection 11C-PIB scans and sMRI provide complementary information in imaging of AD and mild cognitive impairment (MCI) (Jack et al., 2008). Since a dynamic scan of 11C-PIB PET potentially provides both perfusion and PIB retention information, its combination with sMRI might help distinguish AD/MCI from normal subjects. In this study, we investigated whether the complementary information of 11C-pPIB and 11C-aPIB extracted from early and late 11 C-PIB frames could improve classification of AD patients, MCI subjects, and cognitively normal controls (CNs) in conjunction with sMRI. We also compared different combinations of 11C-PIB PET and sMRI measures with respect to their classification performance for distinguishing AD or MCI from CN subjects based on the same subject cohort.

psychological and laboratory examinations at the PLA General Hospital, Beijing, China, before they were included in this study. The clinical diagnosis of all the subjects was made by neurologists with 10 þ years of experience in AD diagnosis and research. A neuropsychological test battery, including the Mini-Mental State Examination (MMSE), Auditory Verbal Learning Test, Geriatric Depression Scale (Yesavage et al., 1982), Clinical Dementia Rating (CDR) (Morris, 1993), Activities of Daily Living scale (ADL), and Montreal Cognitive Assessment (MoCA), was used to test all subjects. Patients diagnosed as probable AD were required to meet the following conditions: diagnosed using the International Classification of Diseases, 10th Revision (ICD-10) for AD; Clinical Dementia Rating (CDR) ¼1 or 2; receiving no nootropic drugs such as anticholinesterase inhibitors; and being able to perform the neuropsychological test and cooperate with MR and PET/CT scanning. The diagnostic criteria for MCI were based upon the criteria of Petersen et al. (1999), including memory complaints lasting at least 6 months; CDR¼0.5; intact functional status and ADL o26; and without dementia according to ICD-10. None of the CN subjects had memory complaints, their general physical status was normal, and CDR¼ 0. Excluding criteria for all subjects were as follows: metabolic conditions such as hypothyroidism or folic acid deficiencies; psychiatric disorders such as schizophrenia or depression; infarction or brain hemorrhage; and Parkinsonian syndrome, epilepsy and other nervous system diseases that can impair cognitive function. The AD and MCI subjects also met the core clinical criteria for probable AD dementia and MCI due to AD as recommended in the recently published diagnostic criteria for AD and MCI due to AD, albeit not all the criteria (Albert et al., 2011; McKhann et al., 2011). All procedures were approved by the Institutional Review Board, and all participants or their appropriate representatives signed informed consent forms after a complete written and verbal description of the study. 2.2. PET/CT acquisition

2. Methods 2.1. Participants Participants comprised 14 AD patients, 12 MCI subjects, and 14 CN subjects, all of whom underwent 11C-PIB PET and sMRI examinations. See Table 1 for demographic data. All participants were right-handed and recruited by advertisement (http://www. 301ad.com.cn, Chinese version). Participants received physical, Table 1 Demographic, clinical and neuropsychological characteristics of subjects.

Gender (M/F) Age (years) Education (years) MMSE

CN (n¼ 14)

MCI (n ¼12)

AD (n ¼14)

P value

5/9 67.4 7 5.0 11.9 7 4.1 28.4 7 1.2 (26–30) 0 3.7 7 1.5

8/4 75.8 7 8.6n 13.3 7 3.8 27.3 7 1.6 (24-30) 0.50 2.3 7 0.9

4/10 68.17 9.9 11.6 7 3.9 19.4 7 3.3nn (13–26) 1.077 0.27 nn 1.7 7 1.3 nn

0.13 0.022 0.52 o 0.001

2.8 7 1.9 5.4 7 1.1

0.3 7 0.5 n 3.6 7 1.8 n

o 0.001 o 0.001

CDR AVLT-Immediate Recall AVLT-Delay Recall 6.1 72.6 AVLT-Recognition 7.5 7 1.7

o 0.001 0.005

Data are mean values7 standard deviation, or number. One-way analysis of variance with Bonferroni post hoc test was used for gender, age, and neuropsychological test comparisons among CN (cognitively normal), MCI (mild cognitive impairment) and AD (Alzheimer’s disease) groups. MMSE, Mini-Mental State Examination; CDR, Clinical Dementia Rating; AVLT, Auditory Verbal Learning test. n

Significant difference compared with CN and AD (Po 0.05). Significant difference compared with CN and MCI (Po 0.05).

nn

All subjects underwent 11C-PIB PET examinations with a Siemens (Biograph Truepoint 64, Siemens Healthcare, Germany) PET/ CT (computed tomography) system under resting conditions with eyes open, low ambient noise and dimmed light. The subject’s head was restricted with a vacuum cushion to minimize movement. 11 C-PIB was synthesized following the same procedure described in a previous study (Philippe et al., 2011) with radiochemical purity of more than 95% and specific activity at 50 GBq/ μmol (1.48 Ci/μmol). The protocol for the 11C-PIB scan included an initial CT acquisition, intravenous tracer injection, and immediately following dynamic PET scan. A CT brain scan was acquired with parameters of 120 kV, 100 mA, and a slice thickness of 3.75 mm, equal to PET’s. Then, a dynamic PET emission scan in three-dimensional acquisition mode started simultaneously with a single intravenous bolus of 11C-PIB at a dose of 4.81–5.55 MBq (0.13–0.15 mCi/kg), and the dynamic brain PET images were collected for 60 min continuously. Finally, the data were reconstructed according to the ordered-subsets expectation maximization (OSEM) algorithm (TrueX algorithm with 3 iterations and 21 subsets) and further binned into 26 frames (1  10 s, 6  5 s, 4  20 s, 2  1 min, 3  2 min, and 10  5 min). A cut-off value of PIB71.15, was obtained based on imaging data of two young volunteers, a 29-year-old man and a 25-yearold woman. Both the young volunteers were healthy and without family history of AD. The cut-off value of computed as the mean cortex SUVR of the young volunteers plus two times the standard deviation (Drzezga et al., 2011; Mormino et al., 2012; Chetelat et al., 2013; Myers et al., 2014).

L. Liu et al. / Psychiatry Research: Neuroimaging 233 (2015) 131–140

2.3. MR imaging Structural MRI was performed using a 3-Tesla GE scanner (Signa HD, Milwaukee, WI, USA) with a standard GE quadrature head coil, and a high-resolution T1-weighted, 3D pulse sequence (radiofrequency-spoiled gradient recall acquisition in the steady state [SPGR], repetition time ¼7.0 ms, echo time ¼2.9 ms, inversion time ¼450 ms, thickness¼1.2 mm, matrix ¼256  256, field of view¼ 240 mm, in-plane resolution¼0.9  0.9 mm2). 2.4. Image analysis The DARTEL toolbox of SPM8 (http://www.fil.ion.ucl.ac.uk/spm) was used for image pre-processing (Ashburner, 2007). All sMRI scans were first used to construct a population template, then spatially normalized to the population template, and finally transformed to the Montreal Neurologic Institute (MNI) space. Jacobian-modulated voxelwise gray matter (GM) tissue density measures were generated for each subject and normalized by multiplying 1,500,000/ICV (the total intracranial volume, mm3). For spatially normalizing the PET scans onto the population template, the sMRI scans were firstly co-registered with the middle frame (the 16th frame) of their respective PET scans. Then, the 4D images were warped to the population template with the deformation fields of their co-registered sMRI images generated in the DARTEL registration procedure. Finally, all images were spatially normalized to the MNI space using DARTEL (Ashburner, 2007). The registration results were visually checked. For each 4D 11 C-PIB scan, a parametric image of 11C-pPIB was generated by summing frames of 8–15 within an optimal time window identified in a previous study (Rostomian et al., 2011). The sum of 11 C-PIB frames within 40–60 min, referred to as 11C-aPIB, was computed to measure amyloid-β plaque deposits (Jack et al., 2008; McNamee et al., 2009). PET images were normalized by the mean of cerebellar GM voxelwise PET values. The cerebellar GM reference region was identified based on the automated anatomic labeling (AAL) atlas (Tzourio-Mazoyer et al., 2002). All the imaging measures in this study were obtained from these normalized PET images and normalized GM tissue density maps. The AAL atlas was also used to identify 90 cortical and subcortical regions of interest (ROIs) by first registering the AAL atlas to the population template space and then to the MNI space subsequently using DARTEL. For all the subjects, each ROI’s GM volume was calculated as the summation of modulated voxelwise GM tissue density values within the ROI considered; the PET value of each ROI was calculated by averaging the ROI’s PET intensities. Therefore, for each subject, we obtained 270 imaging features, including 90 ROI-based features from the sMRI image, 90 ROIbased features from the 11C-pPIB image, and 90 ROI-based features from the 11C-aPIB image. 2.5. Statistical analysis and pattern classification One-way analysis of variance (ANOVA) with the Bonferroni post hoc test was used to compare gender, age, and neuropsychological tests among CN, MCI and AD groups. Two-sample t-tests were used to compare GM tissue density, 11C-pPIB, and 11 C-aPIB measures, smoothed with a 6-mm full-width at halfmaximum isotropic Gaussian kernel, between groups on a voxelwise basis. Statistical parametric maps of group differences were thresholded at a significance value of p o0.05, corrected using the False Discovery Rate (FDR) correction method, and the effects of sex, age, and education were regressed out using a general linear model. A linear sparse support vector machine (SVM), particularly L1regularized SVM, implemented using Liblinear v1.93 toolbox (Fan et al.,

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2008b) with the default parameter setting, i.e., C¼1, was adopted to test the classification performance of imaging measures derived from individual modalities (11C-pPIB, 11C-aPIB, and GM) and the combinations with complementary information (11C-pPIBþ 11C-aPIB, 11 C-pPIBþGM, 11C-aPIBþGM, 11C-pPIBþ 11C-aPIBþGM) for distinguishing AD or MCI from CN subjects at an individual subject level. Each of the features was normalized to have zero mean and unit variance across subjects for the classification. The L1-SVM automatically identifies features discriminative for the classification, i.e., features with non-zero weights in the trained SVM classification model. To relieve the curse of dimensionality, the classification models with multimodality imaging measures were built upon selected features. Particularly, we first trained L1-SVM classification models based on features from individual modalities separately, and then selected features with non-zero weights in each modality-specific SVM classification model. To evaluate the classification performance of different modalities and their combinations, a leave-one-out cross-validation (LOOCV) strategy was adopted. Given a study with n subjects, we performed n training/testing experiments. In each training/testing experiment, one subject was selected as a testing subject for evaluating a classification model built upon all other subjects. The procedure was repeated n times, and at each time a different subject was selected as the testing subject and a different classification model was built upon a different set of training subjects. Each classification model’s sensitivity, specificity, receiver operating characteristic (ROC) curve, and area under a receiver operating characteristic curve (AUC) were determined from the LOOCV prediction values of all subjects. The sensitivity was calculated as the percentage of AD (MCI) subjects classified as AD (MCI) subjects and the specificity as the percentage of CN subjects classified as CN. The contribution of each regional imaging feature to the classification was assessed by its frequency selected by the L1-SVM classification models in the LOOCV procedure. The statistical significance of the classification accuracy was estimated using permutation tests (Ojala and Garriga, 2010). Particularly, the classification accuracy estimated with the LOOCV was used as a statistic, and an empirical estimate of the cumulative distribution of the statistic under the null hypothesis was obtained with 10,000 permutation tests. Each permutation test obtained an estimation of classification accuracy with LOOCV based on the imaging data with their class labels randomly permuted. The pvalue of the classification accuracy was computed as the frequency of the classification accuracy estimated with the actual labels smaller than the statistic estimated with permuted labels.

3. Results 3.1. Age information and cognitive performance The MCI groups (F¼4.23, p ¼0.022) were older than the CN and AD groups. The CN, MCI and AD groups differed in MMSE and ADLT scores. Particularly, the AD group’s cognitive performance (F¼65.93, p¼ 0.000) was significantly worse than that of the MCI and CN groups, but the difference between the MCI and the CN groups was not statistically significant. All the AD patients, nine MCI patients, and five controls were PIBþ subjects. 3.2. Characteristic imaging patterns of AD and MCI Voxel-wise difference maps between the AD and CN groups for C-pPIB, 11C-aPIB, GM tissue density, and voxel-wise difference map between the MCI and CN groups for 11C-aPIB are shown in Fig. 1 (FDR corrected, po 0.05). The significant differences of 11 C-pPIB between AD and CN subjects were mainly located at 11

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Fig. 1. Voxel-wise difference maps between AD and CN for 11C-pPIB (A), 11C-aPIB (B), gray matter tissue density maps (C), and between MCI and CN for 11C-aPIB (D), corrected for FDR and thresholded at p o 0.05. The color bar values indicate the value of the t-statistic. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

temporoparietal, medial temporal cortex (MTC), precuneus, temporal and frontal cortices. For 11C-aPIB, the increased amyloid deposition was found in AD subjects at temporal and frontal cortices, posterior cingulate cortex (PCC), and anterior cingulate cortex (ACC). GM loss in AD relative to CN subjects was statistically significant in bilateral MTC, lateral temporoparietal cortex, precuneus and frontal cortex. Statistically significant group differences between MCI and CN subjects were found in 11C-aPIB measures (FDR corrected, po 0.05) at temporal cortex, precuneus, PCC and ACC. However, no difference was found for the

comparison between MCI and CN subjects at the same statistical significance (FDR corrected, po 0.05) in measures of 11C-pPIB or GM tissue density. 3.3. Classification performance of individual modalities and their combinations Table 2 summarizes the classification performance of individual modalities and their combinations. For the individual modalities, 11 C-pPIB had the best performance for the classification study of

Table 2 Classification performance of cognitively normal CN, mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Imaging parameters

11

C-pPIB C-aPIB MRI 11 C-pPIB þ 11C-aPIB 11 C-pPIB þ MRI 11 C-aPIBþ MRI 11 C-pPIB þ 11C-aPIBþ MRI 11

AD vs. CN

MCI vs. CN

Sensitivity (%)

Specificity (%)

AUC

p-value

Sensitivity (%)

Specificity (%)

AUC

p-value

71.4 57.1 71.4 100 92.9 85.7 100

100 92.9 85.7 100 92.9 78.6 100

0.96 0.92 0.69 1 0.97 0.94 1

0.0043 0.0324 0.0357 o 0.0001 0.0005 0.0142 o 0.0001

50.0 41.7 75.0 41.7 75.0 75.0 75.0

64.3 64.3 42.9 57.1 85.7 57.1 92.9

0.65 0.51 0.67 0.51 0.88 0.73 0.89

0.3350 0.4094 0.3519 0.5023 0.017 0.1612 0.0081

Each row of the table shows sensitivity, specificity, area under the curve and p-value. The numbers of subjects classified as CN, MCI, and AD are also displayed.

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Fig. 2. ROC curves of different classification models for AD classification (A) and MCI classification (B).

AD vs. CN. However, no single modality had a statistically significant performance for the classification of MCI vs. CN. For the combinations of two modalities, 11C-pPIB þ 11C-aPIB had the best performance for the classification of AD vs. CN, while 11C-pPIB combined with sMRI had the highest classification accuracy for the classification of MCI vs. CN. 11C-pPIB þ 11C-aPIB þ sMRI had the overall best classification performance for both the classification studies of AD vs. CN and MCI vs. CN. Particularly, 11 C-pPIB þ 11C-aPIB and 11C-pPIB þ 11C-aPIB þsMRI both achieved a 100% correct classification rate for distinguishing AD from CN subjects, while the best accuracy achieved by an individual modality was 86% (11C-pPIB). For distinguishing MCI from CN subjects, 11 C-pPIB þ 11C-aPIB þsMRI performed the best with a classification accuracy of 85% (sensitivity: 75%, specificity: 93%), better than the performance of any individual modality and the combinations of any two individual ones. ROC curves of these classification models are shown in Fig. 2.

The contribution of regional imaging features to the best classifiers was visualized with their frequency selected by the L1-SVM classification models in the LOOCV procedure. As shown in Fig. 3, both 11C-pPIB and sMRI measures of right hippocampus and left PCC were selected by the classifier of AD vs. CN. The classifier of AD vs. CN also selected 11C-pPIB measures of left ACC, bilateral PCC, and right MTC, 11C-aPIB measures of bilateral thalami, frontal cortex, and caudate, and sMRI measures of left PCC, right hippocampus and bilateral superior occipital cortex. The classifier of MCI vs. CN selected different regional features. Particularly, 11C-pPIB measures of frontal, temporal and parietal cortices, 11C-aPIB measures of left superior occipital, inferior frontal (pars triangularis), and middle temporal pole, and sMRI measures of bilateral PCC, occipital and left inferior parietal cortices contributed more to the classification of MCI vs. CN than other regional measures. The empirical distribution of the classification rates estimated in 10,000 permutation tests for every classification study is shown

Fig. 3. Brain regions with imaging measures selected by the linear sparse SVM classification models with high frequency in AD classification (A) and MCI classification (B). The colorbars indicate the frequency of regions selected by the classification models in the LOOCV procedure. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Fig. 4. Empirical distribution of the classifier accuracy estimated using a leave-one-out cross-validation procedure in 10,000 permutation tests for AD classification (A) and for MCI classification (B). The red circles are the classification accuracy measures estimated based on the actual label information. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

along with the actual LOOCV classification rate in Fig. 4. For each classification study, the mean of the classification rates estimated in permutation tests was around 0.5. For the classification of AD vs. CN, all the p-values of classification accuracy measures were smaller than 0.05, while for the classification of MCI vs. CN, statistically significant classification accuracy estimation was obtained for the classification based on the combination of 11C-pPIB

and sMRI and the combination of three individual modalities.

4. Discussion Many studies have demonstrated that biomarkers from different imaging modalities provide complementary information for

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AD diagnosis (Fan et al., 2008a; Jack et al., 2008; Apostolova et al., 2010; Fan, 2011; Westman et al., 2012; Frisoni et al., 2013). Particularly, sMRI and 11C-PIB PET imaging have been demonstrated to provide complementary information in imaging of AD and MCI groups (Jack et al., 2008). Most 11C-PIB PET-based AD studies have focused on the late frames of 11C-PIB scans, while a few studies have demonstrated that perfusion information, similar to 18F-FDG, can be extracted from early frames of dynamic PET imaging with amyloid tracers, including 11C-PIB and 18F-AV45 (Kim et al., 2009, 2010; Hüll et al., 2011; Meyer et al., 2011; Rostomian et al., 2011; Forsberg et al., 2012; Hsiao et al., 2012; Rodell et al., 2012; Fu et al., 2014). However, no systematic evaluation of sMRI and the imaging measures obtained from both early and late frames of dynamic 11 C-PIB or 18F-AV45 PET scans, as well as their combinations, has been reported for AD diagnosis. In the current study, we adopted a machine learning technique to evaluate these imaging measures for diagnosis of AD and MCI, aiming to obtain an optimal classification scheme for distinguishing individual subjects. The experimental results have demonstrated that the combination of sMRI and both perfusion and amyloid information extracted from dynamic 11C-PIB PET had the best classification performance for distinguishing AD and MCI subjects from CN subjects, providing supportive evidence that the multi-factorial pathology burden of AD-related dementia should be investigated using multiple complementary assessments. Voxel-wise two-sample t-tests of 11C-pPIB, 11C-aPIB and GM tissue density measures between different diagnostic groups revealed perfusion decrease patterns, amyloid deposition patterns, and GM atrophy patterns associated with AD and MCI. As shown in Fig. 1, the AD group showed a significant reduction in perfusion in the frontal and medial temporal regions, largely consistent with recent findings of rCBF-single photon emission computed tomography (SPECT) studies that revealed CBF decreased in bilateral temporoparietal, posterior cingulate, and left frontal cortices of AD patients (Lobotesis et al., 2001). The amyloid deposition pattern of AD was consistent with the statistical parametric mapping results shown in (Jack et al., 2008). The bilateral MTC, lateral temporoparietal cortex, precuneus and frontal cortex were highly susceptible to GM loss in AD patients, consistent with existing findings of GM atrophy in AD studies (Braak and Braak, 1991b; Jack et al., 2008). No statistically significant group difference in GM tissue density measures between MCI and CN subjects was detected, indicating that the MCI subjects in the current study did not have severe GM loss. The GM atrophy pattern of AD was not the same as AD’s amyloid deposition pattern or perfusion distribution pattern, indicating that PET measures and GM volume could provide complementary information for AD diagnosis. The classification performance has been evaluated using LOOCV in conjunction with permutation tests. For the classification of AD vs. CN subjects, 11C-pPIB achieved the highest classification accuracy in individual modalities (85.7%, p ¼ 0.0043), and the combination of 11C-pPIB and 11C-aPIB performed better than other combinations of two measures with respect to the classification accuracy. The dual biomarker of 11C-PIB in conjunction with sMRI achieved the highest classification accuracy (100%, p o0.0001). For the classification of MCI vs. CN subjects, no individual measure achieved a statistically significant classification accuracy (p o0.05). However, 11C-pPIB when combined with sMRI achieved a classification accuracy of 80.8% (p¼ 0.017). Similar to the AD classification, the dual biomarker of 11C-PIB in conjunction with sMRI achieved the best classification accuracy (84.6%, p ¼0.0081). Most of the brain regions contributed to the classification of AD vs. CN and MCI vs. CN were considered to be involved in AD. It is generally considered that AD pathology is complex and evolves as the disease progresses, starting from the transentorhinal and entorhinal cortex to the hippocampus, and then to other parts of the

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Fig. 5. Box plots of regional 11C-aPIB measures and individual data points for 3 ROIs located at parietal, frontal, and lateral temporal lobes. In particular, AAL template was used to identify the ROIs. The ROI at the parietal lobe consisted of angular gyrus; posterior cingulate cortex, precuneus, and Inferior parietal lobule; the ROI at the frontal lobe consisted of orbitofrontal cortex (inferior), orbitofrontal cortex (medial), and inferior frontal gyrus (opercular), and the ROI at lateral temporal lobe consisted of hippocampus, parahippocampal gyrus, and olfactory sulcus. Average imaging measures were calculated for individual subjects within each of the ROIs. The horizontal lines in the box plots represent the 25th, 50th, 75th percentiles. The plotted whiskers extend from each end of the box to the adjacent values in the dataset, which are the most extreme values within 1.5 times the interquartile range from the ends of the box. Outliers, indicated by a star, are data with values beyond the ends of the whiskers. The black pluses indicate mean values. The p value shown in x-axis for each ROI is ANOVA analysis result of the three groups.

limbic system, followed by the primary motor and somatosensory cortices, and finally the occipital cortex (Braak and Braak, 1991b, 1997; Delacourte et al., 1999). Brain atrophy typically appears along the perforant hippocampal pathway (entorhinal cortex, hippocampus and PCC) in early stages of AD, and in temporal, parietal and frontal neocortices in the late stage of AD (Frisoni et al., 2010). A SPECT study revealed that reduced perfusion of AD patients appeared between the entorhinal and limbic stages in the anterior medial temporal lobe, subcallosal area, posterior cingulate cortex, and precuneus (Bradley et al., 2002). Both thalami and caudate nucleus are considered to be only mildly involved in AD. However, several studies have demonstrated the occurrence of considerable amounts of amyloid in the thalami and caudate nucleus of AD patients (Ogomori et al., 1989; Braak and Braak, 1991a). In all the classification studies with individual imaging parameters, 11C-pPIB performed better or equal to sMRI, while 11C-aPIB had the worst performance. As shown in Fig. 5, examination of 11 C-aPIB regional measures of the individual subjects revealed that one of the CN subjects had atypical 11C-aPIB measures in ROIs located at parietal, frontal, and lateral temporal lobes. In particular, the AAL template was used to select the ROIs that were potentially most involved in the AD process (Minoshima et al., 1997; Engler et al., 2006; Devanand et al., 2010). This outlier CN subject was a 71-year-old woman with an MMSE score of 29 and a CDR of 0. Such atypical CN subjects were also reported in previous studies (Rabinovici and Jagust, 2009; Mosconi et al., 2010b; Mosconi and McHugh, 2011b), and rendered the classification of AD or MCI difficult if based solely on 11C-aPIB. Furthermore, the good performance of 11C-pPIB in classifying AD may be related to the disease stage at which a neurodegenerative cascade produced by earlier changes in Aβ results in changes in cerebral perfusion and glucose metabolism. A recent study with arterial spin labeling (ASL) MRI has also indicated that amyloid-β pathology might cause more loss of blood flow in early stages of AD (Mattsson et al.,

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2014). All the classification results indicate that multimodal imaging analysis might be helpful in AD diagnosis, clinical trials, and evaluation of treatment effects. It is worth noting that redundant features may degrade the classification performance due to the curse of dimensionality. Therefore, a linear sparse SVM with L1 regularization was adopted in this study to build classification models (Fan et al., 2008b). The sparse SVM has an inherent feature-selection mechanism to remove features with zero weights in the classification models, different from the widely adopted L2 regularized SVM classification models that typically require a separate feature-selection technique to achieve better classification performance in neuroimaging studies (Fan et al., 2008c) because irrelevant/redundant features may hamper the generalization performance of classification models. As shown in Fig. 3, the regional features automatically selected for the AD and MCI classification, such as hippocampus, PCC, and lateral temporoparietal cortical regions, were known to be affected by AD and MCI (Klunk et al., 2004; Mosconi, 2005; Fan et al., 2008c). With L2 regularized SVMs, 11 C-pPIB þ 11C-aPIB þsMRI also had the best classification accuracy in the classification studies. However, they performed no better than the L1 regularized SVMs. In particular, the best classification rates obtained by the L2 regularized SVMs were 100% (28/28) for the AD classification and 76.92% (20/26) for the MCI classification. The statistical significance of the classification accuracy was assessed using permutation tests. As shown in Fig. 4, for each of the classification studies, the mean of the classification rates estimated in permutation tests was around 0.5. As summarized in Table 2, all the classification accuracy measures for the AD classification were statistically significant (p o0.05). For the MCI classification, statistically significant classification accuracy measures were obtained for the classifiers built on the combination of three biomarkers and the combination of sMRI and 11C-pPIB. The fact that the classification of MCI with high sensitivity and specificity was more difficult than the classification of AD also highlighted that the brain alteration in MCI subjects was mild and multimodality imaging could help in reliably detecting mild alterations (Frisoni et al., 2013). Limitations of the present study include small sample size and age difference across groups. Although we regressed out age, gender and education factors before the statistical comparisons and classifications and used permutation tests to estimate the statistical significance of the classification accuracy, the findings should be further validated based on larger datasets with wellbalanced patient and control groups. The cut-off value of PIB 7 obtained based on imaging data of two young volunteers might not be sufficiently robust, although the value was in the range obtained based on larger cohorts (Drzezga et al., 2011; Mormino et al., 2012; Chetelat et al., 2013; Myers et al., 2014). In summary, the current study has demonstrated that sMRI and both perfusion and amyloid information extracted from dynamic 11 C-PIB PET scans provide complementary, discriminative information for distinguishing AD and MCI individuals from CN subjects. The classification results have demonstrated that perfusion and amyloid deposition information extracted from 11C-PIB performed very well in the classification of AD vs. CN (Meyer et al., 2011; Rostomian et al., 2011; Forsberg et al., 2012), while the combination of 11C-pPIB and sMRI had better performance for the classification of MCI vs. CN, highlighting the potential benefit of a dual biomarker of dynamic 11C-PIB imaging in clinical practice. In conclusion, perfusion and amyloid deposition information of 11 C-PIB in conjunction with sMRI aid in the diagnosis of AD and MCI.

Disclosure statement for authors There are no actual or potential conflicts of interest. All procedures of this study were approved by the Institutional Review Board, and all participants or their appropriate representatives signed informed consent forms after a complete written and verbal description of the study.

Acknowledgments The authors thank Dayi Yin, Jiajin Liu, Can Li for technical support and PET data acquisition; Jian Liu and Yungang Li for helping with the PET radiochemistry; and Pan Wang and Bo Zhou for clinical diagnosis and sMRI acquisition. This study was financially sponsored by the National Natural Science Foundation of China (Grant 30670586, 30900352, 30970770, 30571600, 91132707, and 60831004), the China Postdoctoral Science Foundation (Grant 20090461433), the Hundred Talents Program of the Chinese Academy of Sciences, and the External Cooperation Program of Chinese Academy of Sciences.

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Combination of dynamic (11)C-PIB PET and structural MRI improves diagnosis of Alzheimer's disease.

Structural magnetic resonance imaging (sMRI) is an established technique for measuring brain atrophy, and dynamic positron emission tomography with (1...
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