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

By Robin Wolz, Valtteri Julkunen, Juha Koikkalainen, Eini Niskanen, Daniel Rueckert, Hilkka Soininen, Jyrki Lötjönen. PLoS ONE, 2011

300 citations recorded by the source corpus.

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.

Subfields: Physiology, Psychiatry and Mental health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5a1084c3b4ec0409f9e74d4e46cc2e17caeedb6f

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Citations in the corpus

A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages (2017, unknown): “Double-dipping was quite common in feature selection in older studies (Querbes et al., 2009; Wolz et al., 2011); however, it has become less common as its effects became clearer.”
Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge (2015, methodology): “, 2011) and MCI (Petersen, 2004) were used, which is 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;”
Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects (2014, unknown): “For example, Wolz et al. (2011) used a simple criterion for labeling where a subject who had not converted to AD before July 2011 was labeled as stable MCI.”
Single Subject Prediction of Brain Disorders in Neuroimaging: Promises and Pitfalls (2016, background): “Most of these studies reported modest accuracies around 65– 80% (Plant et al., 2010; Salvatore et al., 2015a; Tangaro et al., 2015; Tong et al., 2014;Wolz et al., 2011; Zu et al., 2015).”