An emphasis in ongoing Alzheimer's disease (AD) research is identifying those biomarkers which best predict future cognitive decline at the various stages of disease progression. These biomarkers can then serve as early markers for diagnosis, and for selection of subjects into clinical trials. Recent results suggest that the identification of such discriminative biomarkers is possible by adapting machine learning methods for this problem: but studies have primarily used modalities in isolation so far. The sensitivity/specificity offered by these methods is unsatisfactory for more clinically relevant questions: which MCI patients will convert to AD? Answering such questions requires new methods that leverage all data sources (e.g., imaging modalities, CSF measures) in conjunction. This project focuses on how data from multiple biomarkers should be optimally aggregated to best predict future cognitive decline, and how these models can improve clinical trials for AD. Hypothesis: Significant improvements in sensitivity and specificity for discriminating AD, MCI, and healthy controls at the level of individual subjects are possible by making use of multiple modalities (together with longitudinal data) simultaneously. Further, these methods will significantly improve sample size estimates in clinical trials, and help derive customized outcomes for evaluating new treatment procedures.
Specific Aims : (1) To develop new image-based machine learning algorithms that can take advantage of multiple modalities simultaneously within a unified framework. (2) To provide a software and extensively evaluate these methods on the ADNI and BLSA datasets, to assess the sensitivity/specificity attainable by truly multi-modal analysis methods. (3) To interface multi-modal classification methods with AD clinical trials: (a) by developing comprehensive sample size estimates needed to observe specific outcomes, and using these methods to derive customized outcomes for an ongoing R01-funded observational/prospective study here at the Wisconsin ADRC. Methods: We will develop new multi-modal machine learning methods that will optimally exploit all data sources simultaneously. Our models will also incorporate longitudinal data, and exploit interaction between modalities at different stages of the disease. This will be used to derive a Multi-Modal Disease Marker (MMDM) (Aim 1). The algorithms will be evaluated on large-scale well-characterized datasets and provided as software tools (Aim 2). We will use these models to improve AD clinical trials in two ways: by sample enrichment and customized outcomes that provide maximum statistical power to detect treatment effects (Aim 3). Significance: This project capitalizes on the Wisconsin ADRC's expertise in machine learning, statistical clinical trial design, imaging, and clinical diagnosis of AD and pre-AD conditions. This project will be the first to implement a multi-modal machine learning metric specifically designed to speed up clinical trials so that potential therapies can be evaluated and an effective treatment arrived at as quickly as possible.
This project will develop novel methods and software systems for highly accurate and automated identification of patterns related to Alzheimer's disease. The techniques will take advantage of the aggregate information from an individual's brain images from multiple imaging modalities such as structural images (e.g., Magnetic Resonance) and functional images (e.g., Positron Emission Tomography), as well as cognitive and demographic information. The software, as a result of successful completion of this project, will provide invaluable assistance in AD diagnosis (and predicting cognitive decline) at the level of individual subjects, as well as allow clinical experiments to be more effective in evaluating which drugs and treatments are effective for such neurological disorders.
| Zhou, Hao Henry; Singh, Vikas; Johnson, Sterling C et al. (2018) Statistical tests and identifiability conditions for pooling and analyzing multisite datasets. Proc Natl Acad Sci U S A 115:1481-1486 |
| Ithapu, Vamsi K; Kondor, Risi; Johnson, Sterling C et al. (2017) The Incremental Multiresolution Matrix Factorization Algorithm. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2017:692-701 |
| Kim, Hyunwoo J; Adluru, Nagesh; Suri, Heemanshu et al. (2017) Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2017:5777-5786 |
| Kim, Won Hwa; Jalal, Mona; Hwang, Seongjae et al. (2017) Online Graph Completion: Multivariate Signal Recovery in Computer Vision. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2017:5019-5027 |
| Gutierrez-Barragan, Felipe; Ithapu, Vamsi K; Hinrichs, Chris et al. (2017) Accelerating permutation testing in voxel-wise analysis through subspace tracking: A new plugin for SnPM. Neuroimage 159:79-98 |
| Hwang, Seong Jae; Adluru, Nagesh; Collins, Maxwell D et al. (2016) Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2016:2517-2525 |
| Ravi, Sathya N; Ithapu, Vamsi K; Johnson, Sterling C et al. (2016) Experimental Design on a Budget for Sparse Linear Models and Applications. JMLR Workshop Conf Proc 48:583-592 |
| Kim, Won Hwa; Hwang, Seong Jae; Adluru, Nagesh et al. (2016) Adaptive Signal Recovery on Graphs via Harmonic Analysis for Experimental Design in Neuroimaging. Comput Vis ECCV 9910:188-205 |
| Kim, Hyunwoo J; Smith, Brandon M; Adluru, Nagesh et al. (2016) Abundant Inverse Regression using Sufficient Reduction and its Applications. Comput Vis ECCV 9907:570-584 |
| Zhou, Hao Henry; Ravi, Sathya N; Ithapu, Vamsi K et al. (2016) Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease. Adv Neural Inf Process Syst 29:2496-2504 |
Showing the most recent 10 out of 36 publications
