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1. Data-driven causal model discovery and personalized prediction in Alzheimer's disease

Author

Haoyang Zheng, Jeffrey R. Petrella, P. Murali Doraiswamy, Guang Lin, Wenrui Hao, and for the Alzheimer’s Disease Neuroimaging Initiative

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract With the explosive growth of biomarker data in Alzheimer’s disease (AD) clinical trials, numerous mathematical models have been developed to characterize disease-relevant biomarker trajectories over time. While some of these models are purely empiric, others are causal, built upon various hypotheses of AD pathophysiology, a complex and incompletely understood area of research. One of the most challenging problems in computational causal modeling is using a purely data-driven approach to derive the model’s parameters and the mathematical model itself, without any prior hypothesis bias. In this paper, we develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer’s Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.

Published
2022
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2. Sex Differences in Cognitive Decline in Subjects with High Likelihood of Mild Cognitive Impairment due to Alzheimer’s disease

Author

Dongwha Sohn, Katie Shpanskaya, Joseph E. Lucas, Jeffrey R. Petrella, Andrew J. Saykin, Rudolph E. Tanzi, Nagiza F. Samatova, and P. Murali Doraiswamy

Subjects
Medicine, Science
Abstract

Abstract Sex differences in Alzheimer’s disease (AD) biology and progression are not yet fully characterized. The goal of this study is to examine the effect of sex on cognitive progression in subjects with high likelihood of mild cognitive impairment (MCI) due to Alzheimer’s and followed up to 10 years in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Cerebrospinal fluid total-tau and amyloid-beta (Aβ42) ratio values were used to sub-classify 559 MCI subjects (216 females, 343 males) as having “high” or “low” likelihood for MCI due to Alzheimer’s. Data were analyzed using mixed-effects models incorporating all follow-ups. The worsening from baseline in Alzheimer’s Disease Assessment Scale-Cognitive score (mean, SD) (9 ± 12) in subjects with high likelihood of MCI due to Alzheimer’s was markedly greater than that in subjects with low likelihood (1 ± 6, p

Published
2018
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3. Sex Differences in Cognitive Decline in Subjects with High Likelihood of Mild Cognitive Impairment due to Alzheimer’s disease

Author

P. Murali Doraiswamy, Nagiza F. Samatova, Katie Shpanskaya, Andrew J. Saykin, Joseph E. Lucas, Rudolph E. Tanzi, Dongwha Sohn, and Jeffrey R. Petrella

Subjects
0301 basic medicine, Male, medicine.medical_specialty, Science, Neuroimaging, Disease, Neuropsychological Tests, Article, 03 medical and health sciences, 0302 clinical medicine, Cognition, Alzheimer Disease, Risk Factors, Internal medicine, mental disorders, medicine, Prevalence, Humans, High likelihood, Cognitive Dysfunction, Longitudinal Studies, Cognitive decline, 10. No inequality, Cognitive impairment, Aged, Retrospective Studies, Aged, 80 and over, Sex Characteristics, Multidisciplinary, Retrospective cohort study, 030104 developmental biology, Disease Progression, Medicine, Female, 030217 neurology & neurosurgery, Sex characteristics
Abstract

Sex differences in Alzheimer’s disease (AD) biology and progression are not yet fully characterized. The goal of this study is to examine the effect of sex on cognitive progression in subjects with high likelihood of mild cognitive impairment (MCI) due to Alzheimer’s and followed up to 10 years in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Cerebrospinal fluid total-tau and amyloid-beta (Aβ42) ratio values were used to sub-classify 559 MCI subjects (216 females, 343 males) as having “high” or “low” likelihood for MCI due to Alzheimer’s. Data were analyzed using mixed-effects models incorporating all follow-ups. The worsening from baseline in Alzheimer’s Disease Assessment Scale-Cognitive score (mean, SD) (9 ± 12) in subjects with high likelihood of MCI due to Alzheimer’s was markedly greater than that in subjects with low likelihood (1 ± 6, p p = 0.006). Our findings highlight the need to further investigate these findings in other populations and develop sex specific timelines for Alzheimer’s disease progression.

Published
2018

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

Author

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

Subjects
0301 basic medicine, Male, Proteomics, Science, Neuroimaging, Disease, Sensitivity and Specificity, Article, 03 medical and health sciences, 0302 clinical medicine, Text mining, Alzheimer Disease, medicine, Humans, Cognitive Dysfunction, Set (psychology), Aged, Aged, 80 and over, Multidisciplinary, Modalities, business.industry, Pattern recognition, Alzheimer’s Disease Neuroimaging Initiative, Genomics, Models, Theoretical, medicine.disease, 030104 developmental biology, Categorization, Case-Control Studies, Medicine, Female, Artificial intelligence, Alzheimer's disease, business, Canonical correlation, 030217 neurology & neurosurgery, Algorithms, Biomarkers
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
2017

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