Deviation From Model of Normal Aging in Alzheimer’s Disease: Application of Deep Learning to Structural MRI Data and Cognitive Tests
Background. Psychophysiological and cognitive tests as well as other functional studies can detect pre-symptomatic stages of dementia. When assembled with structural data, cognitive tests diagnose NDs more reliably thus becoming a multimodal diagnostic tool. Objective. Our main goal is to improve sc...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9773353/ |
| _version_ | 1811255129987350528 |
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| author | Tetiana Habuza Nazar Zaki Elfadil A. Mohamed Yauhen Statsenko |
| author_facet | Tetiana Habuza Nazar Zaki Elfadil A. Mohamed Yauhen Statsenko |
| author_sort | Tetiana Habuza |
| collection | DOAJ |
| description | Background. Psychophysiological and cognitive tests as well as other functional studies can detect pre-symptomatic stages of dementia. When assembled with structural data, cognitive tests diagnose NDs more reliably thus becoming a multimodal diagnostic tool. Objective. Our main goal is to improve screening for dementia by studying an association between the brain structure and its function. Hypothetically, the brain structure-function association has features specific for either disease-related cognitive deterioration or normal neurocognitive slowing while aging. Materials and methods. We studied a total number of 287 cognitively normal cases, 646 of mild cognitive impairment, and 369 of Alzheimer’s disease. To work out a new marker of neurodegeneration, we created a convolutional neural network-based regression model and predicted the cognitive status of the cognitively preserved examinee from the brain MRI data. This was a model of normal aging. A big deviation from the model suggests a high risk of accelerated cognitive decline. Results. The deviation from the model of normal aging can accurately distinguish cognitively normal subjects from MCI patients (AUC = 0.9957). We also achieved creditable performance in the MCI-versus-AD classification (AUC = 0.9793). We identified a considerable difference in the MMSE test between A-positive and A-negative demented individuals according to ATN-criteria (6.27±1.82 vs 5.32±1.9; <inline-formula> <tex-math notation="LaTeX">$p< 0.05$ </tex-math></inline-formula>). Conclusion. The deviation from the model of normal aging can be potentially used as a marker of dementia and as a tool for differentiating Alzheimer’s disease from non-Alzheimer’s dementia. To find and justify a reliable threshold levels, further research is required |
| first_indexed | 2024-04-12T17:19:08Z |
| format | Article |
| id | doaj.art-a274aa8554e847c5bdced7a8c2f809b1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T17:19:08Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a274aa8554e847c5bdced7a8c2f809b12022-12-22T03:23:33ZengIEEEIEEE Access2169-35362022-01-0110532345324910.1109/ACCESS.2022.31746019773353Deviation From Model of Normal Aging in Alzheimer’s Disease: Application of Deep Learning to Structural MRI Data and Cognitive TestsTetiana Habuza0https://orcid.org/0000-0003-1687-6915Nazar Zaki1https://orcid.org/0000-0002-6259-9843Elfadil A. Mohamed2https://orcid.org/0000-0001-9281-3815Yauhen Statsenko3https://orcid.org/0000-0002-7713-3333Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab EmiratesDepartment of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab EmiratesArtificial Intelligence Research Center (AIRC), College of Information Technology, Ajman University, Ajman, United Arab EmiratesRadiology Department, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab EmiratesBackground. Psychophysiological and cognitive tests as well as other functional studies can detect pre-symptomatic stages of dementia. When assembled with structural data, cognitive tests diagnose NDs more reliably thus becoming a multimodal diagnostic tool. Objective. Our main goal is to improve screening for dementia by studying an association between the brain structure and its function. Hypothetically, the brain structure-function association has features specific for either disease-related cognitive deterioration or normal neurocognitive slowing while aging. Materials and methods. We studied a total number of 287 cognitively normal cases, 646 of mild cognitive impairment, and 369 of Alzheimer’s disease. To work out a new marker of neurodegeneration, we created a convolutional neural network-based regression model and predicted the cognitive status of the cognitively preserved examinee from the brain MRI data. This was a model of normal aging. A big deviation from the model suggests a high risk of accelerated cognitive decline. Results. The deviation from the model of normal aging can accurately distinguish cognitively normal subjects from MCI patients (AUC = 0.9957). We also achieved creditable performance in the MCI-versus-AD classification (AUC = 0.9793). We identified a considerable difference in the MMSE test between A-positive and A-negative demented individuals according to ATN-criteria (6.27±1.82 vs 5.32±1.9; <inline-formula> <tex-math notation="LaTeX">$p< 0.05$ </tex-math></inline-formula>). Conclusion. The deviation from the model of normal aging can be potentially used as a marker of dementia and as a tool for differentiating Alzheimer’s disease from non-Alzheimer’s dementia. To find and justify a reliable threshold levels, further research is requiredhttps://ieeexplore.ieee.org/document/9773353/Error of cognitive score predictionbiomarkerAlzheimer’s diseaseneuroimagingconvolutional neural networkdeep learning |
| spellingShingle | Tetiana Habuza Nazar Zaki Elfadil A. Mohamed Yauhen Statsenko Deviation From Model of Normal Aging in Alzheimer’s Disease: Application of Deep Learning to Structural MRI Data and Cognitive Tests IEEE Access Error of cognitive score prediction biomarker Alzheimer’s disease neuroimaging convolutional neural network deep learning |
| title | Deviation From Model of Normal Aging in Alzheimer’s Disease: Application of Deep Learning to Structural MRI Data and Cognitive Tests |
| title_full | Deviation From Model of Normal Aging in Alzheimer’s Disease: Application of Deep Learning to Structural MRI Data and Cognitive Tests |
| title_fullStr | Deviation From Model of Normal Aging in Alzheimer’s Disease: Application of Deep Learning to Structural MRI Data and Cognitive Tests |
| title_full_unstemmed | Deviation From Model of Normal Aging in Alzheimer’s Disease: Application of Deep Learning to Structural MRI Data and Cognitive Tests |
| title_short | Deviation From Model of Normal Aging in Alzheimer’s Disease: Application of Deep Learning to Structural MRI Data and Cognitive Tests |
| title_sort | deviation from model of normal aging in alzheimer x2019 s disease application of deep learning to structural mri data and cognitive tests |
| topic | Error of cognitive score prediction biomarker Alzheimer’s disease neuroimaging convolutional neural network deep learning |
| url | https://ieeexplore.ieee.org/document/9773353/ |
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