Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.
| Original language | English (US) |
|---|---|
| Article number | 1952 |
| Journal | Scientific reports |
| Volume | 9 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 1 2019 |
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ASJC Scopus subject areas
- General
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Predicting Alzheimer’s disease progression using multi-modal deep learning approach. / for Alzheimer’s Disease Neuroimaging Initiative.
In: Scientific reports, Vol. 9, No. 1, 1952, 01.12.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Predicting Alzheimer’s disease progression using multi-modal deep learning approach
AU - for Alzheimer’s Disease Neuroimaging Initiative
AU - Lee, Garam
AU - Nho, Kwangsik
AU - Kang, Byungkon
AU - Sohn, Kyung Ah
AU - Kim, Dokyoon
AU - Weiner, Michael W.
AU - Aisen, Paul
AU - Petersen, Ronald Carl
AU - Jack, Clifford R Jr.
AU - Jagust, William
AU - Trojanowki, John Q.
AU - Toga, Arthur W.
AU - Beckett, Laurel
AU - Green, Robert C.
AU - Saykin, Andrew J.
AU - Morris, John
AU - Shaw, Leslie M.
AU - Khachaturian, Zaven
AU - Sorensen, Greg
AU - Carrillo, Maria
AU - Kuller, Lew
AU - Raichle, Marc
AU - Paul, Steven
AU - Davies, Peter
AU - Fillit, Howard
AU - Hefti, Franz
AU - Holtzman, Davie
AU - Mesulam, M. Marcel
AU - Potter, William
AU - Snyder, Peter
AU - Montine, Tom
AU - Thomas, Ronald G.
AU - Donohue, Michael
AU - Walter, Sarah
AU - Sather, Tamie
AU - Jiminez, Gus
AU - Balasubramanian, Archana B.
AU - Mason, Jennifer
AU - Sim, Iris
AU - Harvey, Danielle
AU - Bernstein, Matthew A
AU - Fox, Nick
AU - Thompson, Paul
AU - Schuff, Norbert
AU - DeCArli, Charles
AU - Borowski, Bret
AU - Jones, David T
AU - Kantarci, Kejal M
AU - Knopman, David S
AU - Graff Radford, Neill R
PY - 2019/12/1
Y1 - 2019/12/1
N2 - Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.
AB - Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer’s Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.
UR - http://www.scopus.com/inward/record.url?scp=85061494936&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061494936&partnerID=8YFLogxK
U2 - 10.1038/s41598-018-37769-z
DO - 10.1038/s41598-018-37769-z
M3 - Article
VL - 9
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 1952
ER -