Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userScalar on time-by-distribution regression and its application for modelling associations between daily-living physical activity and cognitive functions in Alzheimers Disease
73
0
0.0
(
0
)
Added by
Rahul Ghosal
Publication date
2021
fields
Mathematical Statistics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Wearable data is a rich source of information that can provide deeper understanding of links between human behaviours and human health. Existing modelling approaches use wearable data summarized at subject level via scalar summaries using regression techniques, temporal (time-of-day) curves using functional data analysis (FDA), and distributions using distributional data analysis (DDA). We propose to capture temporally local distributional information in wearable data using subject-specific time-by-distribution (TD) data objects. Specifically, we propose scalar on time-by-distribution regression (SOTDR) to model associations between scalar response of interest such as health outcomes or disease status and TD predictors. We show that TD data objects can be parsimoniously represented via a collection of time-varying L-moments that capture distributional changes over the time-of-day. The proposed method is applied to the accelerometry study of mild Alzheimers disease (AD). Mild AD is found to be significantly associated with reduced maximal level of physical activity, particularly during morning hours. It is also demonstrated that TD predictors attain much stronger associations with clinical cognitive scales of attention, verbal memory, and executive function when compared to predictors summarized via scalar total activity counts, temporal functional curves, and quantile functions. Taken together, the present results suggest that the SOTDR analysis provides novel insights into cognitive function and AD.
rate research
Read More
With the advent of continuous health monitoring via wearable devices, users now generate their unique streams of continuous data such as minute-level physical activity or heart rate. Aggregating these streams into scalar summaries ignores the distributional nature of data and often leads to the loss of critical information. We propose to capture the distributional properties of wearable data via user-specific quantile functions that are further used in functional regression and multi-modal distributional modelling. In addition, we propose to encode user-specific distributional information with user-specific L-moments, robust rank-based analogs of traditional moments. Importantly, this L-moment encoding results in mutually consistent functional and distributional interpretation of the results of scalar-on-function regression. We also demonstrate how L-moments can be flexibly employed for analyzing joint and individual sources of variation in multi-modal distributional data. The proposed methods are illustrated in a study of association of accelerometry-derived digital gait biomarkers with Alzheimers disease (AD) and in people with normal cognitive function. Our analysis shows that the proposed quantile-based representation results in a much higher predictive performance compared to simple distributional summaries and attains much stronger associations with clinical cognitive scales.
Accurate diagnosis of Alzheimers Disease (AD) entails clinical evaluation of multiple cognition metrics and biomarkers. Metrics such as the Alzheimers Disease Assessment Scale - Cognitive test (ADAS-cog) comprise multiple subscores that quantify different aspects of a patients cognitive state such as learning, memory, and language production/comprehension. Although computer-aided diagnostic techniques for classification of a patients current disease state exist, they provide little insight into the relationship between changes in brain structure and different aspects of a patients cognitive state that occur over time in AD. We have developed a Convolutional Neural Network architecture that can concurrently predict the trajectories of the 13 subscores comprised by a subjects ADAS-cog examination results from a current minimally preprocessed structural MRI scan up to 36 months from image acquisition time without resorting to manual feature extraction. Mean performance metrics are within range of those of existing techniques that require manual feature selection and are limited to predicting aggregate scores.
Existing studies consider Alzheimers disease (AD) a comorbidity of epilepsy, but also recognize epilepsy to occur more frequently in patients with AD than those without. The goal of this paper is to understand the relationship between epilepsy and AD by studying causal relations among subgroups of epilepsy patients. We develop an approach combining representation learning with tensor factorization to provide an in-depth analysis of the risk factors among epilepsy patients for AD. An epilepsy-AD cohort of ~600,000 patients were extracted from Cerner Health Facts data (50M patients). Our experimental results not only suggested a causal relationship between epilepsy and later onset of AD ( p = 1.92e-51), but also identified five epilepsy subgroups with distinct phenotypic patterns leading to AD. While such findings are preliminary, the proposed method combining representation learning with tensor factorization seems to be an effective approach for risk factor analysis.
Recent evidence has shown that structural magnetic resonance imaging (MRI) is an effective tool for Alzheimers disease (AD) prediction and diagnosis. While traditional MRI-based diagnosis uses images acquired at a single time point, a longitudinal study is more sensitive and accurate in detecting early pathological changes of the AD. Two main difficulties arise in longitudinal MRI-based diagnosis: (1) the inconsistent longitudinal scans among subjects (i.e., different scanning time and different total number of scans); (2) the heterogeneous progressions of high-dimensional regions of interest (ROIs) in MRI. In this work, we propose a novel feature selection and estimation method which can be applied to extract features from the heterogeneous longitudinal MRI. A key ingredient of our method is the combination of smoothing splines and the $l_1$-penalty. We perform experiments on the Alzheimers Disease Neuroimaging Initiative (ADNI) database. The results corroborate the advantages of the proposed method for AD prediction in longitudinal studies.
Alzheimers disease is the most common cause of dementia. It is the fifth-leading cause of death among elderly people. With high genetic heritability (79%), finding disease causal genes is a crucial step in find treatment for AD. Following the International Genomics of Alzheimers Project (IGAP), many disease-associated genes have been identified; however, we dont have enough knowledge about how those disease-associated genes affect gene expression and disease-related pathways. We integrated GWAS summary data from IGAP and five different expression level data by using TWAS method and identified 15 disease causal genes under strict multiple testing (alpha<0.05), 4 genes are newly identified; identified additional 29 potential disease causal genes under false discovery rate(alpha < 0.05), 21 of them are newly identified. Many genes we identified are also associated with some autoimmune disorder.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
