Proceedings ArticleDOI
Heterogeneous data fusion for alzheimer's disease study
Jieping Ye,Kewei Chen,Teresa Wu,Jing Li,Zheng Zhao,Rinkal Patel,Min Bae,Ravi Janardan,Huan Liu,Gene E. Alexander,Eric M. Reiman +10 more
- pp 1025-1033
TLDR
Experimental results show that the integration of multiple data sources leads to a considerable improvement in the prediction accuracy, and the proposed algorithm identifies biomarkers that play more significant roles than others in AD diagnosis.Abstract:
Effective diagnosis of Alzheimer's disease (AD) is of primary importance in biomedical research. Recent studies have demonstrated that neuroimaging parameters are sensitive and consistent measures of AD. In addition, genetic and demographic information have also been successfully used for detecting the onset and progression of AD. The research so far has mainly focused on studying one type of data source only. It is expected that the integration of heterogeneous data (neuroimages, demographic, and genetic measures) will improve the prediction accuracy and enhance knowledge discovery from the data, such as the detection of biomarkers. In this paper, we propose to integrate heterogeneous data for AD prediction based on a kernel method. We further extend the kernel framework for selecting features (biomarkers) from heterogeneous data sources. The proposed method is applied to a collection of MRI data from 59 normal healthy controls and 59 AD patients. The MRI data are pre-processed using tensor factorization. In this study, we treat the complementary voxel-based data and region of interest (ROI) data from MRI as two data sources, and attempt to integrate the complementary information by the proposed method. Experimental results show that the integration of multiple data sources leads to a considerable improvement in the prediction accuracy. Results also show that the proposed algorithm identifies biomarkers that play more significant roles than others in AD diagnosis.read more
Citations
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Journal ArticleDOI
Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment
TL;DR: Three modalities of biomarkers are proposed to combine, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method, and shows considerably better performance, compared to the case of using an individual modality of biomarker.
Journal ArticleDOI
Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database
Rémi Cuingnet,Emilie Gerardin,Jérôme Tessieras,Guillaume Auzias,Stéphane Lehéricy,Marie-Odile Habert,Marie Chupin,Habib Benali,Olivier Colliot +8 more
TL;DR: Evaluated the performance of ten high dimensional classification methods proposed to automatically discriminate between patients with Alzheimer's disease or mild cognitive impairment and elderly controls using 509 subjects from the ADNI database, finding whole-brain methods achieved high accuracies and the use of feature selection did not improve the performance but substantially increased the computation times.
Proceedings ArticleDOI
Mining topic-level influence in heterogeneous networks
TL;DR: A generative graphical model is proposed which utilizes the heterogeneous link information and the textual content associated with each node in the network to mine topic-level direct influence and a topic- level influence propagation and aggregation algorithm is proposed to derive the indirect influence between nodes.
Journal ArticleDOI
Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data.
TL;DR: This paper proposes an incremental method for AD classification using cortical thickness data, and shows that the entorhinal cortex was the most discriminative region for classification, which is consistent with previous pathological findings.
Journal ArticleDOI
Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data
TL;DR: An incomplete Multi-Source Feature (iMSF) learning method where all the samples (with at least one available data source) can be used, and a classifier ensemble is constructed by combining this method with four other methods for missing value estimation.
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