Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease

Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.
PLoS ONE (Impact Factor: 3.23). 10/2011; 6(10):e25446. DOI: 10.1371/journal.pone.0025446
Source: PubMed


The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features.

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Available from: Juha Koikkalainen,
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    • "In the literature, many brain morphometric pattern analysis methods have been developed for computer-aided AD/MCI diagnosis, by identifying differences in shape and neuroanatomical configuration of different brains provided by magnetic resonance imaging (MRI) [2] [3] [4] [5] [6] [7] [8] [9] [10]. Most of early works use regional measurement of anatomical volumes in pre-defined regions of interest (ROIs) (e.g., hippocampus, entorhinal cortex, or neocortex) to investigate abnormal tissue structure caused by AD or MCI. "
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    ABSTRACT: Multi-template based brain morphometric pattern analysis using magnetic resonance imaging (MRI) has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multi-view morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multi-template based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality the underlying data distribution is actually not pre-known. In this paper, we propose an inherent structure based multi-view leaning (ISML) method using multiple templates for AD/MCI classification. Specifically, we first extract multi-view feature representations for subjects using multiple selected templates, and then cluster subjects within a specific class into several sub-classes (i.e., clusters) in each view space. Then, we encode those sub-classes with unique codes by considering both their original class information and their own distribution information, followed by a multi-task feature selection model. Finally, we learn an ensemble of view-specific support vector machine (SVM) classifiers based on their respectively selected features in each view, and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multi-template based methods.
    IEEE transactions on bio-medical engineering 11/2015; DOI:10.1109/TBME.2015.2496233 · 2.35 Impact Factor
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    • ") were used , which is a common practice in studies of computer - aided diagnosis methods ( Cuingnet et al . , 2011 ; Klöppel et al . , 2008 ; Falahati et al . , 2014 ; Davatzikos et al . , 2008a ; Duchesne et al . , 2008 ; Fan et al . , 2008a , b ; Gray et al . , 2013 ; Koikkalainen et al . , 2012 ; Magnin et al . , 2009 ; Vemuri et al . , 2008 ; Wolz et al . , 2011 ) . Ground truth diagnosis of dementia can only be assessed using autopsy and is therefore only rarely available . Of the previously mentioned pa - pers , only one paper included one group of 20 AD patients with an au - topsy confirmed diagnosis ( Klöppel et al . , 2008 ) . Amyloid imaging ( Klunk et al . , 2004 ) has also proven to be "
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    ABSTRACT: Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi- center data set. Using clinical practice as starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer’s disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with in total 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer’s Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework:
    NeuroImage 05/2015; DOI:10.1016/j.neuroimage.2015.01.048 · 6.36 Impact Factor
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    • "Relatively recent literature has shown that pattern analysis methods are powerful diagnostic and predictive tools (Aksu et al., 2011; Costafreda et al., 2011; Davatzikos et al., 2009; Dickerson and Wolk, 2012; Duchesne et al., 2008; Hinrichs et al., 2009; Kloppel et al., 2008; Liu et al., 2004; McEvoy et al., 2009, 2011; Plant et al., 2010; Vemuri et al., 2009b; Wolz et al., 2011). One such index, the SPARE-AD score, calculated using a pattern classification method described in (Davatzikos et al., 2009; Fan et al., 2007), has been previously determined to be a good predictor of MCI to AD conversion (Misra et al., 2009) as well as of conversion from CN to MCI in healthy older adults (Davatzikos et al., 2008b, 2009). "
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    ABSTRACT: This study evaluates the individual, as well as relative and joint value of indices obtained from magnetic resonance imaging (MRI) patterns of brain atrophy (quantified by the SPARE-AD index), cerebrospinal fluid (CSF) biomarkers, APOE genotype, and cognitive performance (ADAS-Cog) in progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) within a variable follow-up period up to 6 years, using data from the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1). SPARE-AD was first established as a highly sensitive and specific MRI-marker of AD vs. cognitively normal (CN) subjects (AUC = 0.98). Baseline predictive values of all aforementioned indices were then compared using survival analysis on 381 MCI subjects. SPARE-AD and ADAS-Cog were found to have similar predictive value, and their combination was significantly better than their individual performance. APOE genotype did not significantly improve prediction, although the combination of SPARE-AD, ADAS-Cog and APOE ε4 provided the highest hazard ratio estimates of 17.8 (last vs. first quartile). In a subset of 192 MCI patients who also had CSF biomarkers, the addition of Aβ1–42, t-tau, and p-tau181p to the previous model did not improve predictive value significantly over SPARE-AD and ADAS-Cog combined. Importantly, in amyloid-negative patients with MCI, SPARE-AD had high predictive power of clinical progression. Our findings suggest that SPARE-AD and ADAS-Cog in combination offer the highest predictive power of conversion from MCI to AD, which is improved, albeit not significantly, by APOE genotype. The finding that SPARE-AD in amyloid-negative MCI patients was predictive of clinical progression is not expected under the amyloid hypothesis and merits further investigation.
    Clinical neuroimaging 12/2014; 4:164–173. DOI:10.1016/j.nicl.2013.11.010 · 2.53 Impact Factor
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