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Multi-modal imaging genetics data fusion by deep auto-encoder and self-representation network for Alzheimer's disease diagnosis and biomarkers extraction.
- Source :
-
Engineering Applications of Artificial Intelligence . Apr2024, Vol. 130, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- Alzheimer's disease (AD) is an incurable neurodegenerative disease, so it is important to intervene in the early stage of the disease. Brain imaging genetics is an effective technique to identify AD-related biomarkers, which can early diagnosis of AD patients once they are clinically verified. With the development of medical imaging and gene sequencing techniques, the association analysis between multi-modal imaging data and genetic data has garnered increasing attention. However, current imaging genetics studies have problem with non-intuitive data fusion. Meanwhile, the characteristics of multi-modal imaging genetics data are high-dimensional, non-linearity, and fewer subjects, so it is necessary to select effective features. In this paper, a multi-modal data fusion framework by deep auto-encoder and self-representation (MFASN) was proposed for early diagnosis of AD. First, a multi-modality brain network was constructed by combining information from the resting-state functional magnetic resonance imaging (fMRI) data and structural magnetic resonance imaging (sMRI) data. Then, we utilized the deep auto-encoder to achieve non-linear transformations and select the informative features. A sparse self-representation module was employed to capture the multi-subspaces structure of the latent representation. At last, a multi-task structured sparse association model was developed to fully mine correlations between the genetic data and multi-modal brain network features. Experiments on AD neuroimaging initiative datasets proved the superiority of the proposed method, while discovering discriminative biomarkers were strongly associated with AD. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09521976
- Volume :
- 130
- Database :
- Academic Search Index
- Journal :
- Engineering Applications of Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 175936573
- Full Text :
- https://doi.org/10.1016/j.engappai.2023.107782