Exploring multifractal-based features for mild Alzheimer's disease classification

• Published in: Magnetic resonance in medicine Vol. 76; no. 1; pp. 259 - 269
• Main Authors: Ni, Huangjing, Zhou, Luping, Ning, Xinbao, Wang, Lei
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.07.2016; Wiley Subscription Services, Inc
• Subjects: Aged; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Brain - diagnostic imaging; classification; Diagnosis, Differential; Female; Fractals; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; multifractal; multiple kernel learning; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Severity of Illness Index; Subtraction Technique; Support Vector Machine; multiple kernel learning; multifractal; classification; Alzheimer's disease
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.25853

Purpose Multifractal applications to resting state functional MRI (rs‐fMRI) time series for diagnosing Alzheimer's disease (AD) are still limited. We aim to address two issues: (I) if and what multifractal features are sufficiently discriminative to detect AD from the healthy; (II) if AD classification could be further improved by combining multifractal features with traditional features in this field. Methods Rs‐fMRI data of 25 AD patients and 38 normal controls were analyzed. A set of multifractal features were systematically investigated. Traditional features in monofractal, linear, and network‐based categories were also extracted for comparison and combination. Both support vector machines and multiple kernel learning (MKL) were used to perform classification with individual and combined features. Results We identified a multifractal feature, Δf, which has the strongest discriminative power among all the features in our study. Moreover, we found that the classification accuracy could be significantly improved from 69% (by Δf only) to up to 76%, when nonsparse MKL is used to combine Δf with the monofractal feature, Hurst. Finally, we showed that incorporating other multifractal features, α(0), Δα and Pc, could also improve traditional‐feature‐based AD classification. Conclusion Our work demonstrated the potential usefulness of multifractal analysis for AD research, especially when combining with the traditional rs‐fMRI features. It contributes to distinguishing AD from NC subjects. Magn Reson Med 76:259–269, 2016. © 2015 Wiley Periodicals, Inc.