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| Diagnosis of Alzheimer′s Disease with Sparse Association of Whole Brain Anatomical Markers |
| Zheng Fei, Tang Qiling*, Liu Ruxuan, Zhang Meiling, Ge Wei |
| (School of Biomedical Engineering, South-Central Minzu University, Wuhan 430074, China) |
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Abstract Alzheimer′s disease (AD) is a complex neurodegenerative disease with progressive impairment of memory and other mental functions, which is the main cause of death in the elderly. How to make an accurate diagnosis of AD is crucial. Existing research methods ignore the relationship between potential features. However, according to biological verification, it is known that many brain regions in the human brain are interconnected anatomically and functionally. Therefore, focusing on the characteristic correlation of different brain regions is beneficial to improve the detection performance of brain cognitive diseases. In this paper, we propose a data-driven method for automatic recognition of anatomical markers in whole brain structural magnetic resonance imaging (sMRI), extract block features based on anatomical points, deeply fuse the features of each block using global correlation, and realize the correlation of various brain regions by calculating the interaction between blocks. Secondly, according to the difference of correlation degree between blocks, thresholding processing is carried out, and redundant information is removed by sparse correlation module to further improve the distinguishing ability of features. Finally, classification model is constructed by using the deep features after sparse to predict Alzheimer′s disease individuals. The experimental results showed that the accuracy and sensitivity of the method reached0.936 8 and 0.921 1, respectively, when the ADNI-1 dataset containing 198 AD patients and 224 healthy subjects were trained, and the ADNI-2 dataset containing 152 AD and 196 healthy subjects were tested. The proposed method takes into account the relationship between blocks and the difference of correlation degree, and can diagnose Alzheimer′s disease more effectively.
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Received: 29 April 2022
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Corresponding Authors:
*E-mail:qltang@mail.scuec.edu.cn
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