Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease
The prognosis and treatment of patients suffering from Alzheimer’s disease (AD) have been among the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify genetic variants associated with brain atrophy. Commonl...
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| Format: | Article |
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MDPI AG
2023-03-01
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| Series: | Genes |
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| Online Access: | https://www.mdpi.com/2073-4425/14/3/626 |
| _version_ | 1797611527036993536 |
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| author | Dipnil Chakraborty Zhong Zhuang Haoran Xue Mark B. Fiecas Xiatong Shen Wei Pan |
| author_facet | Dipnil Chakraborty Zhong Zhuang Haoran Xue Mark B. Fiecas Xiatong Shen Wei Pan |
| author_sort | Dipnil Chakraborty |
| collection | DOAJ |
| description | The prognosis and treatment of patients suffering from Alzheimer’s disease (AD) have been among the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify genetic variants associated with brain atrophy. Commonly, in these analyses, neuroimaging features are extracted based on one of many possible brain atlases with FreeSurf and other popular software; this, however, may cause the loss of important information due to our incomplete knowledge about brain function embedded in these suboptimal atlases. To address the issue, we propose convolutional neural network (CNN) models applied to three-dimensional MRI data for the whole brain or multiple, divided brain regions to perform completely data-driven and automatic feature extraction. These image-derived features are then used as endophenotypes in genome-wide association studies (GWASs) to identify associated genetic variants. When we applied this method to ADNI data, we identified several associated SNPs that have been previously shown to be related to several neurodegenerative/mental disorders, such as AD, depression, and schizophrenia. |
| first_indexed | 2024-03-11T06:29:58Z |
| format | Article |
| id | doaj.art-51e7f95c6c894d52a177ed52f775088e |
| institution | Directory Open Access Journal |
| issn | 2073-4425 |
| language | English |
| last_indexed | 2024-03-11T06:29:58Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Genes |
| spelling | doaj.art-51e7f95c6c894d52a177ed52f775088e2023-11-17T11:17:12ZengMDPI AGGenes2073-44252023-03-0114362610.3390/genes14030626Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s DiseaseDipnil Chakraborty0Zhong Zhuang1Haoran Xue2Mark B. Fiecas3Xiatong Shen4Wei Pan5Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USADepartment of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USADivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USADivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USASchool of Statistics, University of Minnesota, Minneapolis, MN 55455, USADivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USAThe prognosis and treatment of patients suffering from Alzheimer’s disease (AD) have been among the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify genetic variants associated with brain atrophy. Commonly, in these analyses, neuroimaging features are extracted based on one of many possible brain atlases with FreeSurf and other popular software; this, however, may cause the loss of important information due to our incomplete knowledge about brain function embedded in these suboptimal atlases. To address the issue, we propose convolutional neural network (CNN) models applied to three-dimensional MRI data for the whole brain or multiple, divided brain regions to perform completely data-driven and automatic feature extraction. These image-derived features are then used as endophenotypes in genome-wide association studies (GWASs) to identify associated genetic variants. When we applied this method to ADNI data, we identified several associated SNPs that have been previously shown to be related to several neurodegenerative/mental disorders, such as AD, depression, and schizophrenia.https://www.mdpi.com/2073-4425/14/3/626CNNsendophenotypesgenome-wide association study (GWAS)principle components (PCs)SNPs |
| spellingShingle | Dipnil Chakraborty Zhong Zhuang Haoran Xue Mark B. Fiecas Xiatong Shen Wei Pan Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease Genes CNNs endophenotypes genome-wide association study (GWAS) principle components (PCs) SNPs |
| title | Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease |
| title_full | Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease |
| title_fullStr | Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease |
| title_full_unstemmed | Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease |
| title_short | Deep Learning-Based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer’s Disease |
| title_sort | deep learning based feature extraction with mri data in neuroimaging genetics for alzheimer s disease |
| topic | CNNs endophenotypes genome-wide association study (GWAS) principle components (PCs) SNPs |
| url | https://www.mdpi.com/2073-4425/14/3/626 |
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