MCI Identification by Joint Learning on Multiple MRI Data
The identification of subtle brain changes that are associated with mild cognitive impairment (MCI), the at-risk stage of Alzheimer’s disease, is still a challenging task. Different from existing works, which employ multimodal data (e.g., MRI, PET or CSF)
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Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA 2 Division of Psychiatry, Geneva University Hospitals, Switzerland 3 Department of Neuroradiology, University Hospitals of Geneva and Faculty of Medicine of the University of Geneva, Switzerland
Abstract. The identification of subtle brain changes that are associated with mild cognitive impairment (MCI), the at-risk stage of Alzheimer’s disease, is still a challenging task. Different from existing works, which employ multimodal data (e.g., MRI, PET or CSF) to identify MCI subjects from normal elderly controls, we use four MRI sequences, including T1-weighted MRI (T1), Diffusion Tensor Imaging (DTI), Resting-State functional MRI (RS-fMRI) and Arterial Spin Labeling (ASL) perfusion imaging. Since these MRI sequences simultaneously capture various aspects of brain structure and function during clinical routine scan, it simplifies finding the relationship between subjects by incorporating the mutual information among them. To this end, we devise a hypergraph-based semisupervised learning algorithm. In particular, we first construct a hypergraph for each of MRI sequences separately using a star expansion method with both the training and testing data. A centralized learning is then performed to model the optimal relevance between subjects by incorporating mutual information between different MRI sequences. We then combine all centralized hypergraphs by learning the optimal weight of each hypergraph based on the minimum Laplacian. We apply our proposed method on a cohort of 41 consecutive MCI subjects and 63 age-and-gender matched controls with four MRI sequences. Our method achieves at least a 7.61% improvement in classification accuracy compared to state-of-theart methods using multiple MRI data.
1 Introduction Alzheimer’s disease (AD) is the most common form of dementia in elderly over 65 years of age. The number of AD patients has reached 26.6 million in nowadays and is expected to double within the next 20 years, leading to 1 in every 85 people worldwide being affected by AD by 2050. Therefore, the diagnosis of AD at its at-risk stage of mild cognitive impairment (MCI) [7] becomes extremely essential and has attracted extensive research efforts in recent years [11, 9]. Previous studies [10] have shown that structural and functional brain changes may start before clinically converted to AD and can be used as potential biomarkers for MCI identification. Recent studies [4, 11] show great promises for integrating multiple modalities, e.g., MRI, PET and CSF, for improving AD/MCI diagnosis accuracy, and semi-supervised c Springer International Publishing Switzerland 2015 N. Navab et al. (Eds.): MICCAI 2015, Part II, LNCS 9350, pp. 78–85, 2015. DOI: 10.1007/978-3-319-24571-3_10
MCI Identification by Joint MRI Data Learning
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Fig. 1. An overview of the proposed centralized hypergraph learning for MCI diagnosis.
learning for multimodal data has also been investigated [2]. However, in most previous works, modeling the relati
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