Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer’s Disease via Fusion of Clinical, Imaging and Omic Features

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Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer’s Disease via Fusion of Clinical, Imaging and Omic Features

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Title: Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer’s Disease via Fusion of Clinical, Imaging and Omic Features
Author: Singanamalli, Asha; Wang, Haibo; Madabhushi, Anant; Weiner, Michael; Aisen, Paul; Petersen, Ronald; Jack, Clifford; Jagust, William; Trojanowki, John; Toga, Arthur; Beckett, Laurel; Green, Robert; Saykin, Andrew; Morris, John; Shaw, Leslie; Kaye, Jeffrey; Quinn, Joseph; Silbert, Lisa; Lind, Betty; Carter, Raina; Dolen, Sara; Schneider, Lon; Pawluczyk, Sonia; Beccera, Mauricio; Teodoro, Liberty; Spann, Bryan; Brewer, James; Vanderswag, Helen; Fleisher, Adam; Heidebrink, Judith; Lord, Joanne; Mason, Sara; Albers, Colleen; Knopman, David; Johnson, Kris; Doody, Rachelle; Villanueva-Meyer, Javier; Chowdhury, Munir; Rountree, Susan; Dang, Mimi; Stern, Yaakov; Honig, Lawrence; Bell, Karen; Ances, Beau; Carroll, Maria; Creech, Mary; Franklin, Erin; Mintun, Mark; Schneider, Stacy; Oliver, Angela; Marson, Daniel; Griffith, Randall; Clark, David; Geldmacher, David; Brockington, John; Roberson, Erik; Natelson Love, Marissa; Grossman, Hillel; Mitsis, Effie; Shah, Raj; deToledo-Morrell, Leyla; Duara, Ranjan; Varon, Daniel; Greig, Maria; Roberts, Peggy; Albert, Marilyn; Onyike, Chiadi; D’Agostino, Daniel; Kielb, Stephanie; Galvin, James; Cerbone, Brittany; Michel, Christina; Pogorelec, Dana; Rusinek, Henry; de Leon, Mony; Glodzik, Lidia; De Santi, Susan; Doraiswamy, P.; Petrella, Jeffrey; Borges-Neto, Salvador; Wong, Terence; Coleman, Edward; Smith, Charles; Jicha, Greg; Hardy, Peter; Sinha, Partha; Oates, Elizabeth; Conrad, Gary; Porsteinsson, Anton; Goldstein, Bonnie; Martin, Kim; Makino, Kelly; Ismail, M.; Brand, Connie; Mulnard, Ruth; Thai, Gaby; Mc-Adams-Ortiz, Catherine; Womack, Kyle; Mathews, Dana; Quiceno, Mary; Levey, Allan; Lah, James; Cellar, Janet; Burns, Jeffrey; Swerdlow, Russell; Brooks, William; Apostolova, Liana; Tingus, Kathleen; Woo, Ellen; Silverman, Daniel; Lu, Po; Bartzokis, George; Graff-Radford, Neill; Parfitt, Francine; Kendall, Tracy; Johnson, Heather; Farlow, Martin; Marie Hake, Ann; Matthews, Brandy; Brosch, Jared; Herring, Scott; Hunt, Cynthia; Dyck, Christopher; Carson, Richard; MacAvoy, Martha; Varma, Pradeep; Chertkow, Howard; Bergman, Howard; Hosein, Chris; Black, Sandra; Stefanovic, Bojana; Caldwell, Curtis; Robin Hsiung, Ging-Yuek; Feldman, Howard; Mudge, Benita; Assaly, Michele; Finger, Elizabeth; Pasternack, Stephen; Rachisky, Irina; Trost, Dick; Kertesz, Andrew; Bernick, Charles; Munic, Donna; Mesulam, Marek-Marsel; Lipowski, Kristine; Weintraub, Sandra; Bonakdarpour, Borna; Kerwin, Diana; Wu, Chuang-Kuo; Johnson, Nancy; Sadowsky, Carl; Villena, Teresa; Scott Turner, Raymond; Johnson, Kathleen; Reynolds, Brigid; Sperling, Reisa; Johnson, Keith; Marshall, Gad; Yesavage, Jerome; Taylor, Joy; Lane, Barton; Rosen, Allyson; Tinklenberg, Jared; Sabbagh, Marwan; Belden, Christine; Jacobson, Sandra; Sirrel, Sherye; Kowall, Neil; Killiany, Ronald; Budson, Andrew; Norbash, Alexander; Lynn Johnson, Patricia; Obisesan, Thomas; Wolday, Saba; Allard, Joanne; Lerner, Alan; Ogrocki, Paula; Tatsuoka, Curtis; Fatica, Parianne; Fletcher, Evan; Maillard, Pauline; Olichney, John; DeCarli, Charles; Carmichael, Owen; Kittur, Smita; Borrie, Michael; Lee, T-Y; RobBartha; Johnson, Sterling; Asthana, Sanjay; Carlsson, Cynthia; Potkin, Steven; Preda, Adrian; Nguyen, Dana; Tariot, Pierre; Burke, Anna; Trncic, Nadira; Reeder, Stephanie; Bates, Vernice; Capote, Horacio; Rainka, Michelle; Scharre, Douglas; Kataki, Maria; Adeli, Anahita; Zimmerman, Earl; Celmins, Dzintra; Brown, Alice; Pearlson, Godfrey; Blank, Karen; Anderson, Karen; Flashman, Laura; Seltzer, Marc; Hynes, Mary; Santulli, Robert; Sink, Kaycee; Gordineer, Leslie; Williamson, Jeff; Garg, Pradeep; Watkins, Franklin; Ott, Brian; Querfurth, Henry; Tremont, Geoffrey; Salloway, Stephen; Malloy, Paul; Correia, Stephen; Rosen, Howard; Miller, Bruce; Perry, David; Mintzer, Jacobo; Spicer, Kenneth; Bachman, David; Pomara, Nunzio; Hernando, Raymundo; Sarrael, Antero; Relkin, Norman; Chaing, Gloria; Lin, Michael; Ravdin, Lisa; Smith, Amanda; Ashok Raj, Balebail; Fargher, Kristin

Note: Order does not necessarily reflect citation order of authors.

Citation: Singanamalli, A., H. Wang, A. Madabhushi, M. Weiner, P. Aisen, R. Petersen, C. Jack, et al. 2017. “Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer’s Disease via Fusion of Clinical, Imaging and Omic Features.” Scientific Reports 7 (1): 8137. doi:10.1038/s41598-017-03925-0. http://dx.doi.org/10.1038/s41598-017-03925-0.
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Abstract: The introduction of mild cognitive impairment (MCI) as a diagnostic category adds to the challenges of diagnosing Alzheimer’s Disease (AD). No single marker has been proven to accurately categorize patients into their respective diagnostic groups. Thus, previous studies have attempted to develop fused predictors of AD and MCI. These studies have two main limitations. Most do not simultaneously consider all diagnostic categories and provide suboptimal fused representations using the same set of modalities for prediction of all classes. In this work, we present a combined framework, cascaded multiview canonical correlation (CaMCCo), for fusion and cascaded classification that incorporates all diagnostic categories and optimizes classification by selectively combining a subset of modalities at each level of the cascade. CaMCCo is evaluated on a data cohort comprising 149 patients for whom neurophysiological, neuroimaging, proteomic and genomic data were available. Results suggest that fusion of select modalities for each classification task outperforms (mean AUC = 0.92) fusion of all modalities (mean AUC = 0.54) and individual modalities (mean AUC = 0.90, 0.53, 0.71, 0.73, 0.62, 0.68). In addition, CaMCCo outperforms all other multi-class classification methods for MCI prediction (PPV: 0.80 vs. 0.67, 0.63).
Published Version: doi:10.1038/s41598-017-03925-0
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5558022/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:34375092
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