Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI

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Abstract

The application of support vector machine classification (SVM) to combined information from magnetic resonance imaging (MRI) and [F18]fluorodeoxyglucose positron emission tomography (FDG-PET) has been shown to improve detection and differentiation of Alzheimer's disease dementia (AD) and frontotemporal lobar degeneration. To validate this approach for the most frequent dementia syndrome AD, and to test its applicability to multicenter data, we randomly extracted FDG-PET and MRI data of 28 AD patients and 28 healthy control subjects from the database provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and compared them to data of 21 patients with AD and 13 control subjects from our own Leipzig cohort. SVM classification using combined volume-of-interest information from FDG-PET and MRI based on comprehensive quantitative meta-analyses investigating dementia syndromes revealed a higher discrimination accuracy in comparison to single modality classification. For the ADNI dataset accuracy rates of up to 88% and for the Leipzig cohort of up to 100% were obtained. Classifiers trained on the ADNI data discriminated the Leipzig cohorts with an accuracy of 91%. In conclusion, our results suggest SVM classification based on quantitative meta-analyses of multicenter data as a valid method for individual AD diagnosis. Furthermore, combining imaging information from MRI and FDG-PET might substantially improve the accuracy of AD diagnosis.

Introduction

Imaging biomarkers such as regional atrophy as measured by structural magnetic resonance imaging (MRI), glucose hypometabolism as measured by [F18]fluorodeoxyglucose positron emission tomography (FDG-PET) or brain β-amyloid load as measured by PIB- (Pittsburgh compound B) and florbetaben-PET have been reported to be useful in diagnosis and/or differential diagnosis of dementia (Sabri et al., 1999, Sabri et al., 2008, Hoffman et al., 2000, Rosen et al., 2002, Buckner et al., 2005, Diehl et al., 2004, Jeong et al., 2005, Diehl-Schmid et al., 2007, Edison et al., 2007, Fung and Stoeckel, 2007, Jack et al., 2008, Schroeter et al., 2007, Schroeter et al., 2008, Schroeter et al., 2009, Schroeter and Neumann, 2011, Davatzikos et al., 2008, Klöppel et al., 2007, Barthel et al., 2011). Therefore, it is now being suggested to incorporate such imaging markers into criteria for in vivo diagnosis of dementia (Dubois et al., 2007, Kipps et al., 2009).

Most previous biomarker studies focused on one specific biomarker or compared sensitivity and specificity of different single biomarkers (Fung and Stoeckel, 2007, Davatzikos et al., 2008, Habeck et al., 2008, Klöppel et al., 2007, Chaves et al., 2009, Habert et al., 2009, Horn et al., 2009, Ramirez et al., 2009a). Obviously, the combination of biomarkers potentially offers further improvements, and statistical methods such as multivariate pattern analyses using support vector machine (SVM) classifications not only enable automatic classification using one specific biomarker, but also provide a tool to combine two or more different biomarkers within the same classification model. For example, it has been shown that combining information from FDG-PET and MRI substantially improves detection (Hinrichs et al., 2009, Zhang et al., 2011) and differentiation of Alzheimer's disease dementia (AD) and frontotemporal lobar degeneration (Dukart et al., 2011). The volume-of-interest (VOI) approach used in Dukart et al. (2011), although less sensitive compared to whole-brain classification when using a single modality, was far superior to whole-brain classification when combined information from FDG-PET and MRI was used. VOIs used in this study were extracted from two comprehensive systematic and quantitative meta-analyses investigating both dementia syndromes in very large cohorts with anatomical likelihood estimates (ALE) (Schroeter et al., 2007, Schroeter et al., 2009). Accordingly, these VOIs represent the prototypical networks affected by these diseases and are not biased to a specific dataset. Furthermore, the preprocessing algorithm (Dukart et al., 2011) was designed to overcome difficulties which occur due to the use of different scanner types and different scanning sequences with different scaling and resolution. However, the generalizability of the results of that study (Dukart et al., 2011) was highly limited due to the low number of subjects and because data from only one center were used for SVM classification.

The goal of the present study was to further validate the approach proposed by Dukart et al. (2011) and to assess its generalizability to data from multicenter studies. To achieve this, we applied the identical preprocessing and classification algorithm to two different datasets. Classification accuracy results using FDG-PET and MRI data from the Clinic of Cognitive Neurology at the University of Leipzig were compared to data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.adni-info.org). To avoid a classification bias towards the ADNI data (because substantially more control subjects and patients were available in this database than in the Leipzig cohort), we restricted the number of subjects and patients included from this database to make the numbers comparable with the Leipzig cohort. This is important because otherwise a combined classifier from both datasets would have mainly learned the distribution of subject and patient data in the ADNI cohort due to the substantially higher amount of subjects while practically ignoring the single center data. The ADNI database is a free access database containing, besides comprehensive neuropsychological and clinical evaluation, FDG-PET and MRI data of AD patients and healthy control subjects. We hypothesized that multicenter MRI and FDG-PET data obtained using different scanner types and sequences might be used to improve the accuracy of AD diagnosis in single clinical centers. Furthermore, we hypothesized that it would be possible to confirm our recent findings  namely improvement of individual dementia diagnosis with SVM classification of combined MRI and FDG-PET data and the aforementioned VOI approach based on meta-analyses.

Section snippets

Subjects

We analyzed FDG-PET and T1-weighted MRI data of 21 patients (Table 1) with an early stage of probable AD, 14 patients with an early stage of FTLD and 13 control subjects. Patients were recruited from the Clinic of Cognitive Neurology at the University Hospital Leipzig. Probable AD was diagnosed according to the clinical NINCDS-ADRDA criteria (McKhann et al., 1984). Diagnosis of FTLD was based on clinical criteria suggested by Neary et al. (1998). The control group included subjects who visited

Clinical characteristics

Clinical characteristics are illustrated in Table 1. The chi-square test for independent samples did not reveal any statistical differences in sex between the four groups [χ²(3)=5.76; p=0.12]. The ANOVAs revealed significant between-group differences in age, CDR, MMSE and education. AD subjects in the ADNI dataset had, as expected, significantly lower MMSE scores compared with the ADNI control group [t(54)=10.7; p<0.001]. There was no significant difference in the MMSE between AD patients in

Discussion

In our study we investigated classification accuracies for detection of AD using FDG-PET, MRI or combined information from both imaging modalities in two independent cohorts. Thereby, we used VOIs centered to coordinates reported in a comprehensive meta-analysis investigating AD (Schroeter et al., 2009) as features for SVM classification. Very high accuracies for discrimination between AD patients and control subjects were obtained in both cohorts using only FDG-PET or combined information from

Acknowledgment

Juergen Dukart, Henryk Barthel, Arno Villringer, Osama Sabri and Matthias L. Schroeter are supported by the Leipzig Research Center for Civilization Diseases (LIFE) at the University of Leipzig. LIFE is funded by the European Union, the European Regional Development Fund (ERFD) and the Free State of Saxony within the framework of the excellence initiative. Matthias L. Schroeter is also supported by the German consortium for frontotemporal lobar degeneration, funded by the German Federal

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