Predicting conversion to Alzheimer’s disease in individuals with Mild Cognitive Impairment using clinically transferable features
Abstract Patients with Mild Cognitive Impairment (MCI) have an increased risk of Alzheimer’s disease (AD). Early identification of underlying neurodegenerative processes is essential to provide treatment before the disease is well established in the brain. Here we used longitudinal data from the ADN...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-18805-5 |
| _version_ | 1798036364597395456 |
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| author | Ingrid Rye Alexandra Vik Marek Kocinski Alexander S. Lundervold Astri J. Lundervold |
| author_facet | Ingrid Rye Alexandra Vik Marek Kocinski Alexander S. Lundervold Astri J. Lundervold |
| author_sort | Ingrid Rye |
| collection | DOAJ |
| description | Abstract Patients with Mild Cognitive Impairment (MCI) have an increased risk of Alzheimer’s disease (AD). Early identification of underlying neurodegenerative processes is essential to provide treatment before the disease is well established in the brain. Here we used longitudinal data from the ADNI database to investigate prediction of a trajectory towards AD in a group of patients defined as MCI at a baseline examination. One group remained stable over time (sMCI, n = 357) and one converted to AD (cAD, n = 321). By running two independent classification methods within a machine learning framework, with cognitive function, hippocampal volume and genetic APOE status as features, we obtained a cross-validation classification accuracy of about 70%. This level of accuracy was confirmed across different classification methods and validation procedures. Moreover, the sets of misclassified subjects had a large overlap between the two models. Impaired memory function was consistently found to be one of the core symptoms of MCI patients on a trajectory towards AD. The prediction above chance level shown in the present study should inspire further work to develop tools that can aid clinicians in making prognostic decisions. |
| first_indexed | 2024-04-11T21:11:50Z |
| format | Article |
| id | doaj.art-4ce92f170b3b4020b80fb2206a6c59bb |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T21:11:50Z |
| publishDate | 2022-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-4ce92f170b3b4020b80fb2206a6c59bb2022-12-22T04:03:00ZengNature PortfolioScientific Reports2045-23222022-09-0112111110.1038/s41598-022-18805-5Predicting conversion to Alzheimer’s disease in individuals with Mild Cognitive Impairment using clinically transferable featuresIngrid Rye0Alexandra Vik1Marek Kocinski2Alexander S. Lundervold3Astri J. Lundervold4Department of Biological and Medical Psychology, University of BergenMohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University HospitalMohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University HospitalMohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University HospitalDepartment of Biological and Medical Psychology, University of BergenAbstract Patients with Mild Cognitive Impairment (MCI) have an increased risk of Alzheimer’s disease (AD). Early identification of underlying neurodegenerative processes is essential to provide treatment before the disease is well established in the brain. Here we used longitudinal data from the ADNI database to investigate prediction of a trajectory towards AD in a group of patients defined as MCI at a baseline examination. One group remained stable over time (sMCI, n = 357) and one converted to AD (cAD, n = 321). By running two independent classification methods within a machine learning framework, with cognitive function, hippocampal volume and genetic APOE status as features, we obtained a cross-validation classification accuracy of about 70%. This level of accuracy was confirmed across different classification methods and validation procedures. Moreover, the sets of misclassified subjects had a large overlap between the two models. Impaired memory function was consistently found to be one of the core symptoms of MCI patients on a trajectory towards AD. The prediction above chance level shown in the present study should inspire further work to develop tools that can aid clinicians in making prognostic decisions.https://doi.org/10.1038/s41598-022-18805-5 |
| spellingShingle | Ingrid Rye Alexandra Vik Marek Kocinski Alexander S. Lundervold Astri J. Lundervold Predicting conversion to Alzheimer’s disease in individuals with Mild Cognitive Impairment using clinically transferable features Scientific Reports |
| title | Predicting conversion to Alzheimer’s disease in individuals with Mild Cognitive Impairment using clinically transferable features |
| title_full | Predicting conversion to Alzheimer’s disease in individuals with Mild Cognitive Impairment using clinically transferable features |
| title_fullStr | Predicting conversion to Alzheimer’s disease in individuals with Mild Cognitive Impairment using clinically transferable features |
| title_full_unstemmed | Predicting conversion to Alzheimer’s disease in individuals with Mild Cognitive Impairment using clinically transferable features |
| title_short | Predicting conversion to Alzheimer’s disease in individuals with Mild Cognitive Impairment using clinically transferable features |
| title_sort | predicting conversion to alzheimer s disease in individuals with mild cognitive impairment using clinically transferable features |
| url | https://doi.org/10.1038/s41598-022-18805-5 |
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