Classification of Mild Cognitive Impairment by Fusing Neuroimaging and Gene Expression Data
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Date
2021-07-02Author
Lyu, Yanjun
Yu, Xiaowei
Zhang, Lu
Zhu, Dajiang
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Show full item recordAbstract
As reversing the pathology of Alzheimer’s disease (AD) is impossible, the diagnosis of mild cognitive impairment (MCI), which is
considered as the precursor of AD, has become a more tractable goal.
Because both brain structural and functional alterations have been
observed in MCI patients, many multimodal fusion approaches have
been proposed to classify MCI from normal controls (NC) in clinical
studies. Given the complex relationships between brain structure
and function, deep learning based models can be helpful in revealing
potential non-linear relationships buried in multimodal neuroimaging data. Meanwhile, RNA expression microarray profile can be
a complementary feature in brain diseases analysis from another
aspect, that is, the knowledge from molecular biology and genetics
may benefit the classification of AD/MCI patients. To incorporate
both imaging and molecular biomarkers, we propose a new deep fusion model: by integrating a cross-model deep network working on
multi-modal brain image data and a fully-connected neural network
working on gene expression data, a parameter representing the ratio of imaging and genetic features can be learned automatically
during the classification process. Using the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) dataset, this method achieves an
overall 82.3% accuracy, by fusing brain structural and functional
connectivity as well as gene expression intensity information.