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1. Integrating Static and Dynamic Data for Improved Prediction of Cognitive Declines Using Augmented Genotype-Phenotype Representations

   Seo, Hoon; Brand, Lodewijk; Wang, Hua; Nie, Feiping (January 2021, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   Alzheimer’s Disease (AD) is a chronic neurodegenerative disease that causes severe problems in patients’ thinking, memory, and behavior. An early diagnosis is crucial to prevent AD progression; to this end, many algorithmic approaches have recently been proposed to predict cognitive decline. However, these predictive models often fail to integrate heterogeneous genetic and neuroimaging biomarkers and struggle to handle missing data. In this work we propose a novel objective function and an associated optimization algorithm to identify cognitive decline related to AD. Our approach is designed to incorporate dynamic neuroimaging data by way of a participant-specific augmentation combined with multimodal data integration aligned via a regression task. Our approach, in order to incorporate additional side-information, utilizes structured regularization techniques popularized in recent AD literature. Armed with the fixed-length vector representation learned from the multimodal dynamic and static modalities, conventional machine learning methods can be used to predict the clinical outcomes associated with AD. Our experimental results show that the proposed augmentation model improves the prediction performance on cognitive assessment scores for a collection of popular machine learning algorithms. The results of our approach are interpreted to validate existing genetic and neuroimaging biomarkers that have been shown to be predictive of cognitive decline. 

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2. A Deep-Learning Approach for the Prediction of Mini-Mental State Examination Scores in a Multimodal Longitudinal Study

   https://doi.org/10.1109/CSCI51800.2020.00144  

   Morar, Ulyana; Martin, Harold; Izquierdo, Walter; Forouzannezhad, Parisa; Zarafshan, Elaheh; Curiel, Rosie E.; Roselli, Monica; Loewenstein, David; Duara, Ranjan; Unger, Elona; et al (December 2020, 2020 International Conference on Computational Science and Computational Intelligence (CSCI))

   null (Ed.)

   This study introduces a new multimodal deep regression method to predict cognitive test score in a 5-year longitudinal study on Alzheimer’s disease (AD). The proposed model takes advantage of multimodal data that includes cerebrospinal fluid (CSF) levels of tau and beta-amyloid, structural measures from magnetic resonance imaging (MRI), functional and metabolic measures from positron emission tomography (PET), and cognitive scores from neuropsychological tests (Cog), all with the aim of achieving highly accurate predictions of future Mini-Mental State Examination (MMSE) test scores up to five years after baseline biomarker collection. A novel data augmentation technique is leveraged to increase the numbers of training samples without relying on synthetic data. With the proposed method, the best and most encompassing regressor is shown to achieve better than state-of-the-art correlations of 85.07%(SD=1.59) for 6 months in the future, 87.39% (SD =1.48) for 12 months, 84.78% (SD=2.66) for 18 months, 85.13% (SD=2.19) for 24 months, 81.15% (SD=5.48) for 30 months, 81.17% (SD=4.44) for 36 months, 79.25% (SD=5.85) for 42 months, 78.98% (SD=5.79) for 48 months, 78.93%(SD=5.76) for 54 months, and 74.96% (SD=7.54) for 60 months. 

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3. Predicting progressions of cognitive outcomes via high-order multi-modal multi-task feature learning

   https://doi.org/10.1109/ISBI.2018.8363635  

   Lu, Lyujian; Wang, Hua; Yao, Xiaohui; Risacher, Shannon; Saykin, Andrew; Shen, Li (April 2018, The Proceedings of IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018))

   Many existing studies on complex brain disorders, such as Alzheimer's Disease, usually employed regression analysis to associate the neuroimaging measures to cognitive status. However, whether these measures in multiple modalities have the predictive power to infer the trajectory of cognitive performance over time still remain under-explored. In this paper, we propose a high-order multi-modal multi-mask feature learning model to uncover temporal relationship between the longitudinal neuroimaging measures and progressive cognitive output scores. The regularizations through sparsity-induced norms implemented in the proposed learning model enable the selection of only a small number of imaging features over time and capture modality structures for multi-modal imaging markers. The promising experimental results in extensive empirical studies performed on the ADNI cohort have validated the effectiveness of the proposed method. 

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4. Joint Multi-Modal Longitudinal Regression and Classification for Alzheimer’s Disease Prediction

   https://doi.org/10.1109/TMI.2019.2958943  

   Brand, Lodewijk; Nichols, Kai; Wang, Hua; Shen, Li; Huang, Heng (January 2020, IEEE Transactions on Medical Imaging)

   Alzheimer’s disease (AD) is a serious neurodegenerative condition that affects millions of individuals across the world. As the average age of individuals in the United States and the world increases, the prevalence of AD will continue to grow. To address this public health problem, the research community has developed computational approaches to sift through various aspects of clinical data and uncover their insights, among which one of the most challenging problem is to determine the biological mechanisms that cause AD to develop. To study this problem, in this paper we present a novel Joint Multi-Modal Longitudinal Regression and Classification method and show how it can be used to identify the cognitive status of the participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and the underlying biological mechanisms. By intelligently combining clinical data of various modalities (i.e., genetic information and brain scans) using a variety of regularizations that can identify AD-relevant biomarkers, we perform the regression and classification tasks simultaneously. Because the proposed objective is a non-smooth optimization problem that is difficult to solve in general, we derive an efficient iterative algorithm and rigorously prove its convergence. To validate our new method in predicting the cognitive scores of patients and their clinical diagnosis, we conduct comprehensive experiments on the ADNI cohort. Our promising results demonstrate the benefits and flexibility of the proposed method. We anticipate that our new method is of interest to clinical communities beyond AD research and have open-sourced the code of our method online.C 

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5. A framework of regularized low-rank matrix models for regression and classification

   https://doi.org/10.1007/s11222-023-10318-z  

   Huang, Hsin-Hsiung; Yu, Feng; Fan, Xing; Zhang, Teng (February 2024, Statistics and Computing)

   While matrix-covariate regression models have been studied in many existing works, classical statistical and computational methods for the analysis of the regression coefficient estimation are highly affected by high dimensional matrix-valued covariates. To address these issues, this paper proposes a framework of matrix-covariate regression models based on a low-rank constraint and an additional regularization term for structured signals, with considerations of models of both continuous and binary responses. We propose an efficient Riemannian-steepest-descent algorithm for regression coefficient estimation. We prove that the consistency of the proposed estimator is in the order of O(sqrt{r(q+m)+p}/sqrt{n}), where r is the rank, p x m is the dimension of the coefficient matrix and p is the dimension of the coefficient vector. When the rank r is small, this rate improves over O(sqrt{qm+p}/sqrt{n}), the consistency of the existing work (Li et al. in Electron J Stat 15:1909-1950, 2021) that does not apply a rank constraint. In addition, we prove that all accumulation points of the iterates have similar estimation errors asymptotically and substantially attaining the minimax rate. We validate the proposed method through a simulated dataset on two-dimensional shape images and two real datasets of brain signals and microscopic leucorrhea images. 

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