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Sci Rep. 2019 Feb 13;9(1):1952. doi: 10.1038/s41598-018-37769-z.

Predicting Alzheimer's disease progression using multi-modal deep learning approach.

Collaborators (309)

Weiner MW, Aisen P, Petersen R, Jack CR Jr, Jagust W, Trojanowki JQ, Toga AW, Beckett L, Green RC, Saykin AJ, Morris J, Shaw LM, Khachaturian Z, Sorensen G, Carrillo M, Kuller L, Raichle M, Paul S, Davies P, Fillit H, Hefti F, Holtzman D, Mesulam MM, Potter W, Snyder P, Montine T, Thomas RG, Donohue M, Walter S, Sather T, Jiminez G, Balasubramanian AB, Mason J, Sim I, Harvey D, Bernstein M, Fox N, Thompson P, Schuff N, DeCArli C, Borowski B, Gunter J, Senjem M, Vemuri P, Jones D, Kantarci K, Ward C, Koeppe RA, Foster N, Reiman EM, Chen K, Mathis C, Landau S, Cairns NJ, Householder E, Taylor-Reinwald L, Lee V, Korecka M, Figurski M, Crawford K, Neu S, Foroud TM, Potkin S, Shen L, Faber K, Kim S, Tha L, Frank R, Hsiao J, Kaye J, Quinn J, Silbert L, Lind B, Carter R, Dolen S, Ances B, Carroll M, Creech ML, Franklin E, Mintun MA, Schneider S, Oliver A, Schneider LS, Pawluczyk S, Beccera M, Teodoro L, Spann BM, Brewer J, Vanderswag H, Fleisher A, Marson D, Griffith R, Clark D, Geldmacher D, Brockington J, Roberson E, Love MN, Heidebrink JL, Lord JL, Mason SS, Albers CS, Knopman D, Johnson K, Grossman H, Mitsis E, Shah RC, deToledo-Morrell L, Doody RS, Villanueva-Meyer J, Chowdhury M, Rountree S, Dang M, Duara R, Varon D, Greig MT, Roberts P, Stern Y, Honig LS, Bell KL, Albert M, Onyike C, D'Agostino D 2nd, Kielb S, Galvin JE, Cerbone B, Michel CA, Pogorelec DM, Rusinek H, de Leon MJ, Glodzik L, De Santi S, Womack K, Mathews D, Quiceno M, Doraiswamy PM, Petrella JR, Borges-Neto S, Wong TZ, Coleman E, Levey AI, Lah JJ, Cella JS, Burns JM, Swerdlow RH, Brooks WM, Arnold SE, Karlawish JH, Wolk D, Clark CM, Apostolova L, Tingus K, Woo E, Silverman DHS, Lu PH, Bartzokis G, Smith CD, Jicha G, Hardy P, Sinha P, Oates E, Conrad G, Graff-Radford NR, Parfitt F, Kendall T, Johnson H, Lopez OL, Oakley M, Simpson DM, Farlow MR, Hake AM, Matthews BR, Brosch JR, Herring S, Hunt C, Porsteinsson AP, Goldstein BS, Martin K, Makino KM, Ismail MS, Brand C, Mulnard RA, Thai G, Mc-Adams-Ortiz C, van Dyck CH, Carson RE, MacAvoy MG, Varma P, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic B, Caldwell C, Hsiung GR, Feldman H, Mudge B, Assaly M, Finger E, Pasternack S, Rachisky I, Trost D, Kertesz A, Bernick C, Munic D, Lipowski K, Weintraub M, Bonakdarpour B, Kerwin D, Wu CK, Johnson N, Sadowsky C, Villena T, Turner RS, Johnson K, Reynolds B, Sperling RA, Johnson KA, Marshall G, Yesavage J, Taylor JL, Lane B, Rosen A, Tinklenberg J, Sabbagh MN, Belden CM, Jacobson SA, Sirrel SA, Kowall N, Killiany R, Budson AE, Norbash A, Johnson PL, Obisesan TO, Wolday S, Allard J, Lerner A, Ogrocki P, Tatsuoka C, Fatica P, Fletcher E, Maillard P, Olichney J, Carmichael O, Kittur S, Borrie M, Lee TY, Bartha R, Johnson S, Asthana S, Carlsson CM, Preda A, Nguyen D, Tariot P, Burke A, Trncic N, Fleisher A, Reeder S, Bates V, Capote H, Rainka M, Scharre DW, Kataki M, Adeli A, Zimmerman EA, Celmins D, Brown AD, Pearlson GD, Blank K, Anderson K, Flashman LA, Seltzer M, Hynes ML, Santulli RB, Sink KM, Gordineer L, Williamson JD, Garg P, Watkins F, Ott BR, Querfurth H, Tremont G, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Perry D, Mintzer J, Spicer K, Bachman D, Finger E, Pasternak S, Rachinsky I, Rogers J, Drost D, Pomara N, Hernando R, Sarrael A, Schultz SK, Ponto LLB, Shim H, Smith KE, Relkin N, Chaing G, Lin M, Ravdin L, Smith A, Raj BA, Fargher K.

Author information

1
Department of Software and Computer Engineering, Ajou University, Suwon, South Korea.
2
Biomedical & Translational Informatics Institute, Geisinger, Danville, USA.
3
Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.
4
Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
5
Department of Software and Computer Engineering, Ajou University, Suwon, South Korea. kasohn@ajou.ac.kr.
6
Biomedical & Translational Informatics Institute, Geisinger, Danville, USA. dkim@geisinger.edu.
7
The Huck Institute of the Life Sciences, Pennsylvania State University, University Park, USA. dkim@geisinger.edu.

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.

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