Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis
Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, and it is difficult to predict individual progression trajectory from normal or mildly impaired cognition to DAT. An in-depth examination of multiple modalities of data may yield an accurate estimate of tim...
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| Published in | Neurobiology of aging Vol. 121; pp. 139 - 156 |
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| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.01.2023
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, and it is difficult to predict individual progression trajectory from normal or mildly impaired cognition to DAT. An in-depth examination of multiple modalities of data may yield an accurate estimate of time-to-conversion to DAT for preclinical subjects at various stages of disease development. We used a deep-learning model designed for survival analyses to predict subjects’ time-to-conversion to DAT using the baseline data of 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our study demonstrated that CDC data outperform genetic or MRI data in predicting DAT time-to-conversion for subjects with Mild Cognitive Impairment (MCI). On the other hand, genetic data provided the most predictive power for subjects with Normal Cognition (NC) at the time of the visit. Furthermore, combining MRI and genetic features improved the time-to-event prediction over using either modality alone. Finally, adding CDC to any combination of features only worked as well as using only the CDC features. |
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| AbstractList | Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, and it is difficult to predict individual progression trajectory from normal or mildly impaired cognition to DAT. An in-depth examination of multiple modalities of data may yield an accurate estimate of time-to-conversion to DAT for preclinical subjects at various stages of disease development. We used a deep-learning model designed for survival analyses to predict subjects' time-to-conversion to DAT using the baseline data of 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our study demonstrated that CDC data outperform genetic or MRI data in predicting DAT time-to-conversion for subjects with Mild Cognitive Impairment (MCI). On the other hand, genetic data provided the most predictive power for subjects with Normal Cognition (NC) at the time of the visit. Furthermore, combining MRI and genetic features improved the time-to-event prediction over using either modality alone. Finally, adding CDC to any combination of features only worked as well as using only the CDC features. Dementia of Alzheimer’s Type (DAT) is a complex disorder influenced by numerous factors, and it is difficult to predict individual progression trajectory from normal or mildly impaired cognition to DAT. An in-depth examination of multiple modalities of data may yield an accurate estimate of time-to-conversion to DAT for preclinical subjects at various stages of disease development. We used a deep-learning model designed for survival analyses to predict subjects’ time-to-conversion to DAT using the baseline data of 401 subjects with 63 features from MRI, genetic, and CDC ( C ognitive tests, D emographic, and C SF) data in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our study demonstrated that CDC data outperform genetic or MRI data in predicting DAT time-to-conversion for subjects with Mild Cognitive Impairment (MCI). On the other hand, genetic data provided the most predictive power for subjects with Normal Cognition (NC) at the time of the visit. Furthermore, combining MRI and genetic features improved the time-to-event prediction over using either modality alone. Finally, adding CDC to any combination of features only worked as well as using only the CDC features. Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, and it is difficult to predict individual progression trajectory from normal or mildly impaired cognition to DAT. An in-depth examination of multiple modalities of data may yield an accurate estimate of time-to-conversion to DAT for preclinical subjects at various stages of disease development. We used a deep-learning model designed for survival analyses to predict subjects' time-to-conversion to DAT using the baseline data of 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our study demonstrated that CDC data outperform genetic or MRI data in predicting DAT time-to-conversion for subjects with Mild Cognitive Impairment (MCI). On the other hand, genetic data provided the most predictive power for subjects with Normal Cognition (NC) at the time of the visit. Furthermore, combining MRI and genetic features improved the time-to-event prediction over using either modality alone. Finally, adding CDC to any combination of features only worked as well as using only the CDC features.Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, and it is difficult to predict individual progression trajectory from normal or mildly impaired cognition to DAT. An in-depth examination of multiple modalities of data may yield an accurate estimate of time-to-conversion to DAT for preclinical subjects at various stages of disease development. We used a deep-learning model designed for survival analyses to predict subjects' time-to-conversion to DAT using the baseline data of 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our study demonstrated that CDC data outperform genetic or MRI data in predicting DAT time-to-conversion for subjects with Mild Cognitive Impairment (MCI). On the other hand, genetic data provided the most predictive power for subjects with Normal Cognition (NC) at the time of the visit. Furthermore, combining MRI and genetic features improved the time-to-event prediction over using either modality alone. Finally, adding CDC to any combination of features only worked as well as using only the CDC features. |
| Author | Mirabnahrazam, Ghazal Popuri, Karteek Lee, Hyunwoo Ma, Da Cao, Jiguo Galvin, James E Beaulac, Cédric Wang, Lei Beg, Mirza Faisal Lee, Sieun |
| AuthorAffiliation | f Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada h Psychiatry and Behavioral Health, Ohio State University Wexner Medical Center, Columbus, OH, USA g Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada a School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada c Department of Mathematics and Statistics, University of Victoria, Victoria, British Columbia, Canada e Department of Computer Science, Memorial University of Newfoundland, St. John’s, Newfoundland & Labrador, Canada b School of Medicine, Wake Forest University, Winston-Salem, NC, USA d Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK i Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA |
| AuthorAffiliation_xml | – name: c Department of Mathematics and Statistics, University of Victoria, Victoria, British Columbia, Canada – name: g Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada – name: b School of Medicine, Wake Forest University, Winston-Salem, NC, USA – name: f Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada – name: e Department of Computer Science, Memorial University of Newfoundland, St. John’s, Newfoundland & Labrador, Canada – name: i Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA – name: a School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada – name: h Psychiatry and Behavioral Health, Ohio State University Wexner Medical Center, Columbus, OH, USA – name: d Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK |
| Author_xml | – sequence: 1 givenname: Ghazal orcidid: 0000-0003-3263-465X surname: Mirabnahrazam fullname: Mirabnahrazam, Ghazal organization: School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada – sequence: 2 givenname: Da surname: Ma fullname: Ma, Da email: dma@wakehealth.edu organization: School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada – sequence: 3 givenname: Cédric surname: Beaulac fullname: Beaulac, Cédric organization: School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada – sequence: 4 givenname: Sieun surname: Lee fullname: Lee, Sieun organization: School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada – sequence: 5 givenname: Karteek surname: Popuri fullname: Popuri, Karteek organization: School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada – sequence: 6 givenname: Hyunwoo surname: Lee fullname: Lee, Hyunwoo organization: Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada – sequence: 7 givenname: Jiguo surname: Cao fullname: Cao, Jiguo organization: Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada – sequence: 8 givenname: James E surname: Galvin fullname: Galvin, James E organization: Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA – sequence: 9 givenname: Lei surname: Wang fullname: Wang, Lei organization: Psychiatry and Behavioral Health, Ohio State University Wexner Medical Center, Columbus, OH, USA – sequence: 10 givenname: Mirza Faisal orcidid: 0000-0003-4229-9613 surname: Beg fullname: Beg, Mirza Faisal email: faisal-lab@sfu.ca organization: School of Engineering, Simon Fraser University, Burnaby, British Columbia, Canada |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36442416$$D View this record in MEDLINE/PubMed |
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| Keywords | Deep learning Survival analysis Multi-modal data Neuroimage genomics Alzheimer's disease Early detection |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Data used in preparation of this article were obtained from the Alzheimer’s disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf First Author |
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| Snippet | Dementia of Alzheimer's Type (DAT) is a complex disorder influenced by numerous factors, and it is difficult to predict individual progression trajectory from... Dementia of Alzheimer’s Type (DAT) is a complex disorder influenced by numerous factors, and it is difficult to predict individual progression trajectory from... |
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| SubjectTerms | Alzheimer Disease - diagnostic imaging Alzheimer Disease - genetics Alzheimer's disease Cognitive Dysfunction - diagnosis Cognitive Dysfunction - genetics Deep learning Disease Progression Early detection Humans Magnetic Resonance Imaging - methods Multi-modal data Neuroimage genomics Neuroimaging - methods Survival Analysis |
| Title | Predicting time-to-conversion for dementia of Alzheimer's type using multi-modal deep survival analysis |
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