Predicting Alzheimer’s disease progression using multi-modal deep learning approach
Authors: Lee Garam , Nho Kwangsik , Kang Byungkon , Sohn Kyung-Ah , Kim Dokyoon ,   for Alzheimer’s Disease Neuroimaging Initiative, Weiner Michael W. , Aisen Paul , Petersen Ronald , Jack Clifford R. , Jagust William , Trojanowki John Q. , Toga Arthur W. , Beckett Laurel , Green Robert C. , Saykin Andrew J. , Morris John , Shaw Leslie M. , Khachaturian Zaven , Sorensen Greg , Carrillo Maria , Kuller Lew , Raichle Marc , Paul Steven , Davies Peter , Fillit Howard , Hefti Franz , Holtzman Davie , Mesulam M. Marcel , Potter William , Snyder Peter , Montine Tom , Thomas Ronald G. , Donohue Michael , Walter Sarah , Sather Tamie , Jiminez Gus , Balasubramanian Archana B. , Mason Jennifer , Sim Iris , Harvey Danielle , Bernstein Matthew , Fox Nick , Thompson Paul , Schuff Norbert , DeCArli Charles , Borowski Bret , Gunter Jeff , Senjem Matt , Vemuri Prashanthi , Jones David , Kantarci Kejal , Ward Chad , Koeppe Robert A. , Foster Norm , Reiman Eric M. , Chen Kewei , Mathis Chet , Landau Susan , Cairns Nigel J. , Householder Erin , Taylor-Reinwald Lisa , Lee Virginia , Korecka Magdalena , Figurski Michal , Crawford Karen , Neu Scott , Foroud Tatiana M. , Potkin Steven , Shen Li , Faber Kelley , Kim Sungeun , Tha Lean , Frank Richard , Hsiao John , Kaye Jeffrey , Quinn Joseph , Silbert Lisa , Lind Betty , Carter Raina , Dolen Sara , Ances Beau , Carroll Maria , Creech Mary L. , Franklin Erin , Mintun Mark A. , Schneider Stacy , Oliver Angela , Schneider Lon S. , Pawluczyk Sonia , Beccera Mauricio , Teodoro Liberty , Spann Bryan M. , Brewer James , Vanderswag Helen , Fleisher Adam , Marson Daniel , Griffith Randall , Clark David , Geldmacher David , Brockington John , Roberson Erik , Love Marissa Natelson , Heidebrink Judith L. , Lord Joanne L. , Mason Sara S. , Albers Colleen S. , Knopman David , Johnson Kris , Grossman Hillel , Mitsis Effie , Shah Raj C. , deToledo-Morrell Leyla , Doody Rachelle S. , Villanueva-Meyer Javier , Chowdhury Munir , Rountree Susan , Dang Mimi , Duara Ranjan , Varon Daniel , Greig Maria T. , Roberts Peggy , Stern Yaakov , Honig Lawrence S. , Bell Karen L. , Albert Marilyn , Onyike Chiadi , D’Agostino Daniel , Kielb Stephanie , Galvin James E. , Cerbone Brittany , Michel Christina A. , Pogorelec Dana M. , Rusinek Henry , de Leon Mony J. , Glodzik Lidia , De Santi Susan , Womack Kyle , Mathews Dana , Quiceno Mary , Doraiswamy P. Murali , Petrella Jeffrey R. , Borges-Neto Salvador , Wong Terence Z. , Coleman Edward , Levey Allan I. , Lah James J. , Cella Janet S. , Burns Jeffrey M. , Swerdlow Russell H. , Brooks William M. , Arnold Steven E. , Karlawish Jason H. , Wolk David , Clark Christopher M. , Apostolova Liana , Tingus Kathleen , Woo Ellen , Silverman Daniel H. S. , Lu Po H. , Bartzokis George , Smith Charles D. , Jicha Greg , Hardy Peter , Sinha Partha , Oates Elizabeth , Conrad Gary , Graff-Radford Neill R. , Parfitt Francine , Kendall Tracy , Johnson Heather , Lopez Oscar L. , Oakley MaryAnn , Simpson Donna M. , Farlow Martin R. , Hake Ann Marie , Matthews Brandy R. , Brosch Jared R. , Herring Scott , Hunt Cynthia , Porsteinsson Anton P. , Goldstein Bonnie S. , Martin Kim , Makino Kelly M. , Ismail M. Saleem , Brand Connie , Mulnard Ruth A. , Thai Gaby , Mc-Adams-Ortiz Catherine , van Dyck Christopher H. , Carson Richard E. , MacAvoy Martha G. , Varma Pradeep , Chertkow Howard , Bergman Howard , Hosein Chris , Black Sandra , Stefanovic Bojana , Caldwell Curtis , Hsiung Ging-Yuek Robin , Feldman Howard , Mudge Benita , Assaly Michele , Finger Elizabeth , Pasternack Stephen , Rachisky Irina , Trost Dick , Kertesz Andrew , Bernick Charles , Munic Donna , Lipowski Kristine , Weintraub Masandra , Bonakdarpour Borna , Kerwin Diana , Wu Chuang-Kuo , Johnson Nancy , Sadowsky Carl , Villena Teresa , Turner Raymond Scott , Johnson Kathleen , Reynolds Brigid , Sperling Reisa A. , Johnson Keith A. , Marshall Gad , Yesavage Jerome , Taylor Joy L. , Lane Barton , Rosen Allyson , Tinklenberg Jared , Sabbagh Marwan N. , Belden Christine M. , Jacobson Sandra A. , Sirrel Sherye A. , Kowall Neil , Killiany Ronald , Budson Andrew E. , Norbash Alexander , Johnson Patricia Lynn , Obisesan Thomas O. , Wolday Saba , Allard Joanne , Lerner Alan , Ogrocki Paula , Tatsuoka Curtis , Fatica Parianne , Fletcher Evan , Maillard Pauline , Olichney John , Carmichael Owen , Kittur Smita , Borrie Michael , Lee T.-Y. , Bartha Rob , Johnson Sterling , Asthana Sanjay , Carlsson Cynthia M. , Preda Adrian , Nguyen Dana , Tariot Pierre , Burke Anna , Trncic Nadira , Fleisher Adam , Reeder Stephanie , Bates Vernice , Capote Horacio , Rainka Michelle , Scharre Douglas W. , Kataki Maria , Adeli Anahita , Zimmerman Earl A. , Celmins Dzintra , Brown Alice D. , Pearlson Godfrey D. , Blank Karen , Anderson Karen , Flashman Laura A. , Seltzer Marc , Hynes Mary L. , Santulli Robert B. , Sink Kaycee M. , Gordineer Leslie , Williamson Jeff D. , Garg Pradeep , Watkins Franklin , Ott Brian R. , Querfurth Henry , Tremont Geoffrey , Salloway Stephen , Malloy Paul , Correia Stephen , Rosen Howard J. , Miller Bruce L. , Perry David , Mintzer Jacobo , Spicer Kenneth , Bachman David , Finger Elizabether , Pasternak Stephen , Rachinsky Irina , Rogers John , Drost Dick , Pomara Nunzio , Hernando Raymundo , Sarrael Antero , Schultz Susan K. , Ponto Laura L. Boles , Shim Hyungsub , Smith Karen Ekstam , Relkin Norman , Chaing Gloria , Lin Michael , Ravdin Lisa , Smith Amanda , Raj Balebail Ashok , Fargher Kristin 
Published: February 2019

AbstractAlzheimer’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.