MCI Identification by Joint Learning on Multiple MRI Data

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 semi-supervised 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-the-art methods using multiple MRI data.

Multimodal Hyper-connectivity Networks for MCI Classification

“…For the n -th subject with P ROIs, a hyper-graph G n = ( V n , E n ) with P vertices can be constructed with each of its vertices representing an ROI. We employed a star expansion method [6] to generate hyper-edges among vertices. Specifically, for each distance matrix, a vertex was first selected as the centroid vertex and a hyper-edge was then constructed by linking the centroid vertex to its nearest neighbors within φ d̄ distance [6].…”

“…We employed a star expansion method [6] to generate hyper-edges among vertices. Specifically, for each distance matrix, a vertex was first selected as the centroid vertex and a hyper-edge was then constructed by linking the centroid vertex to its nearest neighbors within φ d̄ distance [6]. Here, d̄ is the average anatomically weighted distance between regions and φ, which was set to 0.78 via grid search on training data, is a hyper-parameter controlling the sparsity of the hyper-network.…”

“…Specifically, the multimodal hyper-connectivity network was constructed using a star expansion method [6] based on the anatomically weighted functional distance between pairs of brain regions. The anatomically weighted functional distance, which is defined as the strength of the anatomically weighted functional connectivity (awFC) [7], was computed using the complementary information conveyed by the rs-fMRI and DTI data.…”

A Novel Dynamic Hyper-graph Inference Framework for Computer Assisted Diagnosis of Neuro-Diseases

“…(2) only considers one modality imaging data, in order to apply hyper-graph for multiple modality imaging data, Gao et.al. [5] proposed to combine multiple hyper-graph models by a linear model and estimating the unknown data labels on the combined multiple hyper-graph structures. Sup-pose there are M modalities data, they construct M different hyper-graph Laplacian matrixes L m , m = 1 , ⋯, M .…”

Landmark-Based Alzheimer’s Disease Diagnosis Using Longitudinal Structural MR Images

“…Gerardin et al extracted features based on hippocampal shape for the purpose of classifying AD and MCI [11]. Gao et al proposed to use hypergraph learning for MCI classification and indexing [12,13]. Kloppe et al proposed to use voxel-based gray matter features for AD classification [14].…”