Multimodal classification of Alzheimer's disease and mild cognitive impairment

Abstract

Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attentions recently. So far, multiple biomarkers have been shown sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51 AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI nonconverters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined

Simultaneous segmentation and grading of anatomical structures for patient's classification: Application to Alzheimer's disease

“…This result is similar to the highest classification accuracy 93.3% reported on a similar sized subset of ADNI (51 AD and 52 CN) in Zhang et al (2011). However, Zhang et al (2011) used a multimodal approach involving positron emission tomography (PET) and cerebro-spinal fluid (CSF) markers to reach this degree of accuracy. By using only MRI, their method based on volumetric features provided an accuracy of 86.2%.…”

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

“…In future research, we plan to use the cortical thickness and diffusion properties (Irimia, Torgerson, Goh, & Van Horn, 2015) and to combine MRI features with nonimaging characteristics (DeCarlo, Tuokko, Williams, Dixon, & MacDonald, 2014), not only to address this limitation but also to further develop the brain‐age technique toward the goal of revealing the earliest indications of AD in symptomatic and asymptomatic individuals. Several studies have successfully investigated multimodal data such as MRI, PET for developing a high accurate AD classification or MCI conversion prediction frameworks (Ortiz, Munilla, Álvarez‐Illán, Górriz, & Ramírez, 2015; Zhang & Shen, 2012; Zhang, Wang, Zhou, Yuan, & Shen, 2011). Another direction for future study may be to use multimodal data (i.e., MRI and PET) to present a high accurate and robust brain‐age model.…”

Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease

“…(2011) using a different database reported up to 81% sensitivity and 95% specificity. Similar or slightly lower results were found for methods relying on tissue segmentation (Davatzikos et al., 2008; Fan, Resnick, Wu, & Davatzikos, 2008; Westman, Aguilar, Muehlboeck, & Simmons, 2013; Zhang, Wang, Zhou, Yuan, & Shen, 2011), elastic deformations (Magnin et al., 2009), semiautomatic segmentation of the hippocampus (Barnes et al., 2004), or combinations of one or more of them (Farhan, Fahiem, & Tauseef, 2014; Kloppel et al., 2008; Plant et al., 2010; Teipel et al., 2007; Wolz et al., 2011). …”