Annals of Applied Statistics最新文献

BAYESIAN EMPIRICAL LIKELIHOOD FOR ULTRA-HIGH DIMENSIONAL VARIABLE SELECTION WITH APPLICATION TO DRUG SENSITIVITY. 超高维变量选择的贝叶斯经验似然方法及其在药物敏感性研究中的应用。

IF 1.5 4区数学

Annals of Applied Statistics Pub Date : 2026-09-01 Epub Date: 2026-09-28 DOI: 10.1214/26-aoas2210

Can Xu, Zeyu Lu, Yichen Cheng, Qifan Song, Yichuan Zhao, Xinlei Wang

{"title":"BAYESIAN EMPIRICAL LIKELIHOOD FOR ULTRA-HIGH DIMENSIONAL VARIABLE SELECTION WITH APPLICATION TO DRUG SENSITIVITY.","authors":"Can Xu, Zeyu Lu, Yichen Cheng, Qifan Song, Yichuan Zhao, Xinlei Wang","doi":"10.1214/26-aoas2210","DOIUrl":"10.1214/26-aoas2210","url":null,"abstract":"Predicting drug sensitivity and selecting informative biomarkers are fundamental challenges in precision oncology. These tasks are complicated by the ultra-high dimensional nature of omics data, where the number of covariates <math><mi>p</mi></math> far exceeds the sample size <math><mi>n</mi></math> . While variable selection methods are widely used, traditional high-dimensional regression techniques often rely on the assumption of Gaussian noise, which is frequently violated in real-world omics datasets. To overcome these limitations, we propose a Bayesian empirical likelihood approach (BEL_HD) for variable selection and prediction in a semiparametric framework. BEL_HD avoids strict distributional assumptions by utilizing estimating equations, and imposes joint regularization on both regression coefficients and the Lagrange multipliers to promote sparsity. We also introduce an efficient active set MCMC algorithm that enables scalable inference in ultra-high dimensional spaces. Through simulations and real-data applications, including a case study on leukemia drug sensitivity, BEL_HD demonstrates superior predictive performance and selects biologically interpretable features. Our method combines the flexibility of empirical likelihood with the inferential benefits of Bayesian modeling, offering a robust and extensible tool for high-dimensional biomedical research.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"20 3","pages":"2364-2385"},"PeriodicalIF":1.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13618762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149076586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DEEP LEARNING OF SEMI-COMPETING RISK DATA VIA A NEW NEURAL EXPECTATION-MAXIMIZATION ALGORITHM. 基于一种新的神经期望最大化算法的半竞争风险数据深度学习。

IF 1.5 4区数学

Annals of Applied Statistics Pub Date : 2026-09-01 Epub Date: 2026-09-28 DOI: 10.1214/26-aoas2190

Stephen Salerno, Zhilin Zhang, Yi Li

{"title":"DEEP LEARNING OF SEMI-COMPETING RISK DATA VIA A NEW NEURAL EXPECTATION-MAXIMIZATION ALGORITHM.","authors":"Stephen Salerno, Zhilin Zhang, Yi Li","doi":"10.1214/26-aoas2190","DOIUrl":"10.1214/26-aoas2190","url":null,"abstract":"Prognostication for lung cancer, a leading cause of mortality, remains a complex task, as it needs to quantify the associations of risk factors and health events spanning a patient's entire life. One challenge is that an individual's disease course involves non-terminal (e.g., disease progression) and terminal (e.g., death) events, which form semi-competing relationships. Our motivation comes from the Boston Lung Cancer Study, a large lung cancer survival cohort, which investigates how risk factors influence a patient's disease trajectory. Following developments in the prediction of time-to-event outcomes with neural networks, deep learning has become a focal area for the development of risk prediction methods in survival analysis. However, limited work has been done to predict multi-state or semi-competing risk outcomes, where a patient may experience adverse events such as disease progression prior to death. We propose a neural expectation-maximization algorithm for semi-competing risks to bridge the gap between classical semi-competing survival models and deep learning. Our algorithm enables estimation of the nonparametric baseline hazards of each state transition, risk functions of predictors, and the degree of dependence among different transitions, via a multitask deep neural network with transition-specific sub-architectures. We apply our method to the Boston Lung Cancer Study and investigate the impact of clinical and genetic predictors on disease progression and mortality.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"20 3","pages":"2453-2473"},"PeriodicalIF":1.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13621910/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149082749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DOMAIN-AWARE MATRIX COMPLETION FOR PHENOTYPE IMPUTATION USING ELECTRONIC HEALTH RECORD DATA WITH APPLICATIONS IN GENOMIC RESEARCH. 利用电子健康记录数据进行表型估算的领域感知矩阵完成，并在基因组研究中应用。

IF 1.5 4区数学

Annals of Applied Statistics Pub Date : 2026-06-01 Epub Date: 2026-06-22 DOI: 10.1214/26-AOAS2165

Hanqing Wu, Cue Hyunkyu Lee, Najmeh Abiri, Iuliana Ionita-Laza

{"title":"DOMAIN-AWARE MATRIX COMPLETION FOR PHENOTYPE IMPUTATION USING ELECTRONIC HEALTH RECORD DATA WITH APPLICATIONS IN GENOMIC RESEARCH.","authors":"Hanqing Wu, Cue Hyunkyu Lee, Najmeh Abiri, Iuliana Ionita-Laza","doi":"10.1214/26-AOAS2165","DOIUrl":"10.1214/26-AOAS2165","url":null,"abstract":"Large-scale biobanks and electronic health records (EHR) offer great opportunities for next-generation genetic studies. However, missing phenotype data is a pervasive feature of EHR, leading to low power of such studies. One promising solution is prediction-powered inference, where statistical or machine learning models are employed to impute phenotypes prior to performing genetic analyses. Although many such methods exist, they tend to be generic and do not incorporate domain-aware knowledge to optimize their performance for downstream genetic analyses. We propose a novel matrix completion method, covImpute, which, unlike generic matrix completion methods such as softImpute, incorporates external information in the form of a genetic covariance matrix among phenotypic features and imputes missing entries with latent genetic components. We compare covImpute with existing methods, including a domain-aware liability threshold model LTPI, and generic softImpute and deep learning autoencoder models in simulations under different missingness mechanisms with respect to power in downstream genetic analyses. In applications to several diseases in UK Biobank, we show that genetically informed methods, such as covImpute and LTPI, can perform substantially better in terms of power of genetic association studies relative to generic imputation models currently in use. Moreover, compared to LTPI, covImpute's flexible framework for incorporating external covariance information provides a more general approach with applicability beyond genetics.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"20 2","pages":"1010-1032"},"PeriodicalIF":1.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13496295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148801055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention. 模型不可知的纵向变量重要性轨迹在自杀预防中的应用总结。

IF 1.5 4区数学

Annals of Applied Statistics Pub Date : 2026-06-01 Epub Date: 2026-06-22 DOI: 10.1214/26-aoas2186

Brian D Williamson, Erica E M Moodie, Gregory E Simon, Rebecca C Rossom, Susan M Shortreed

{"title":"Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention.","authors":"Brian D Williamson, Erica E M Moodie, Gregory E Simon, Rebecca C Rossom, Susan M Shortreed","doi":"10.1214/26-aoas2186","DOIUrl":"10.1214/26-aoas2186","url":null,"abstract":"Risk of suicide attempt varies over time. Understanding the importance of risk factors measured at a mental health visit can help clinicians evaluate future risk and provide appropriate care during the visit. In prediction settings where data are collected over time, such as in mental health care, it is often of interest to understand both the importance of variables for predicting the response at each time point and the importance summarized over the time series. Building on recent advances in estimation and inference for variable importance measures, we define summaries of variable importance trajectories and corresponding estimators. The same approaches for inference can be applied to these measures regardless of the choice of the algorithm(s) used to estimate the prediction function. We propose a nonparametric efficient estimation and inference procedure as well as a null hypothesis testing procedure that are valid even when complex machine learning tools are used for prediction. Through simulations, we demonstrate that our proposed procedures have good operating characteristics. We use these approaches to analyze electronic health records data from two large health systems to investigate the longitudinal importance of risk factors for suicide attempt to inform future suicide prevention research and clinical workflow.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"20 2","pages":"1340-1363"},"PeriodicalIF":1.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13299308/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148347169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EVALUATING MULTIPLEX DIAGNOSTIC TEST USING PARTIALLY ORDERED BAYES CLASSIFIER. 用部分有序贝叶斯分类器评价多重诊断试验。

IF 1.5 4区数学

Annals of Applied Statistics Pub Date : 2026-06-01 Epub Date: 2026-06-22 DOI: 10.1214/26-aoas2156

Ying Kuen Cheung, Louise Kuhn

{"title":"EVALUATING MULTIPLEX DIAGNOSTIC TEST USING PARTIALLY ORDERED BAYES CLASSIFIER.","authors":"Ying Kuen Cheung, Louise Kuhn","doi":"10.1214/26-aoas2156","DOIUrl":"10.1214/26-aoas2156","url":null,"abstract":"In a diagnostic test using multiplex assay, each individual biomarker is often expected to have monotonic association with the disease outcome and therefore the underlying disease classification rule is partially ordered with respect to the biomarkers. Nonparametric estimation of the classification rule can be accomplished by projecting an unconstrained Bayes estimator onto the partial ordering subspace. However, computing the projection is challenging as it involves performing maximization over a constrained parameter space whose size grows exponentially with the sample size. We introduce a novel sequential update method for projection-based nonparametric estimation of the disease classification rule and propose new recursive algorithms to implement the method. The proposed algorithms yields the exact Bayes solution that maximizes the posterior gain with respect to a classification-type gain function. When compared to an existing algorithm that gives approximate Bayes solution, our algorithms accomplish the same tasks with much reduced elapsed time in simulation study. We apply the sequential update method to evaluate a human papillomavirus test for cervical cancer precursor lesions and derive diagnostic rule that improves accuracy on existing estimation methods including parametric logistic regression and monotone generalized additive models.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"20 2","pages":"1283-1300"},"PeriodicalIF":1.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13308686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148347159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A NOVEL BAYESIAN FRAMEWORK UNCOVERING BRAIN CONNECTIVITY-TO-SHAPE RELATIONSHIP IN PRECLINICAL ALZHEIMER'S DISEASE. 一个新的贝叶斯框架揭示了临床前阿尔茨海默病中大脑连接与形状的关系。

IF 1.5 4区数学

Annals of Applied Statistics Pub Date : 2026-06-01 Epub Date: 2026-06-22 DOI: 10.1214/26-aoas2154

Shengxian Ding, Emily Johns, Anton Orlichenko, Carolyn Fredericks, Yize Zhao

{"title":"A NOVEL BAYESIAN FRAMEWORK UNCOVERING BRAIN CONNECTIVITY-TO-SHAPE RELATIONSHIP IN PRECLINICAL ALZHEIMER'S DISEASE.","authors":"Shengxian Ding, Emily Johns, Anton Orlichenko, Carolyn Fredericks, Yize Zhao","doi":"10.1214/26-aoas2154","DOIUrl":"10.1214/26-aoas2154","url":null,"abstract":"Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-beta plaques and tau tangles, with significant pathological changes occurring in subcortical brain regions. While previous research has focused primarily on volumetric reductions in areas such as the hippocampus, thalamus, and caudate, emerging evidence suggests that their fine-grained shape deformations may offer greater sensitivity to early disease pathology. Moreover, understanding how these shape alterations influence brain functional connectivity (FC) networks could provide critical insights into the neurobiological mechanisms underlying the progression of AD. In this context, we propose a novel statistical approach, the Connectivity-on-Shape Regression (COSR) model, designed to investigate the spatially varying impact of brain subcortical shape on FC, accounting for the intrinsic modularity of functional networks. Under a Bayesian framework, COSR employs a relaxed-thresholded Gaussian process prior model to promote feature selection and integrates a stochastic block model to capture the unknown modular organization of FC. To facilitate the practical application of COSR with vertex-level shape measurements, we develop a computationally efficient variational inference approach to achieve posterior inference. Extensive simulations demonstrate the superiority of COSR over existing alternatives in accurately uncovering connectivity-to-shape associations and identifying neurobiological signals. Applying COSR to data from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's study, we discover meaningful neural structural-functional relationships in amyloid-positive individuals, highlighting the potentially complex interplay between structural and functional brain alterations during this crucial preclinical stage of AD.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"20 2","pages":"1429-1451"},"PeriodicalIF":1.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13306255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148347212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DYNAMIC CLASSIFICATION OF LATENT DISEASE PROGRESSION WITH AUXILIARY SURROGATE LABELS. 用辅助替代标签动态分类潜伏性疾病进展。

IF 1.5 4区数学

Annals of Applied Statistics Pub Date : 2026-03-01 Epub Date: 2026-03-20 DOI: 10.1214/26-aoas2150

Zexi Cai, Donglin Zeng, Karen S Marder, Lawrence S Honig, Yuanjia Wang

{"title":"DYNAMIC CLASSIFICATION OF LATENT DISEASE PROGRESSION WITH AUXILIARY SURROGATE LABELS.","authors":"Zexi Cai, Donglin Zeng, Karen S Marder, Lawrence S Honig, Yuanjia Wang","doi":"10.1214/26-aoas2150","DOIUrl":"10.1214/26-aoas2150","url":null,"abstract":"Disease progression prediction based on patients' evolving health information is challenging when true disease states are unknown due to diagnostic capabilities or high costs. For example, the absence of gold-standard neurological diagnoses hinders distinguishing Alzheimer's disease (AD) from related conditions such as AD-related dementias (ADRDs), including Lewy body dementia (LBD). Combining temporally dependent surrogate labels and health markers may improve disease prediction. However, existing literature models informative surrogate labels and observed variables that reflect the underlying states using purely generative approaches, often posing unrealistic assumptions on the outcomes and suffering from misspecification thereof. We propose integrating the conventional hidden Markov model as a generative model with a time-varying discriminative classification model to simultaneously handle potentially misspecified surrogate labels and incorporate important markers of disease progression. We develop an adaptive forward-backward algorithm with subjective labels for estimation, and utilize the modified posterior and Viterbi algorithms to predict the progression of future states or new patients based on objective markers only. Importantly, the adaptation eliminates the need to model the marginal distribution of longitudinal markers, a requirement in traditional algorithms. Asymptotic properties are established, and significant improvements in finite samples are demonstrated via simulation studies. Analysis of the neuropathological dataset of the National Alzheimer's Coordinating Center (NACC) shows much improved accuracy in distinguishing LBD from AD.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"20 1","pages":"641-662"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13004507/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147500503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MODEL-FREE INFERENCE FOR CHARACTERIZING PROTEIN MUTATIONS THROUGH A COEVOLUTIONARY LENS. 通过共同进化透镜描述蛋白质突变的无模型推断。

IF 1.5 4区数学

Annals of Applied Statistics Pub Date : 2026-03-01 Epub Date: 2026-03-20 DOI: 10.1214/26-aoas2145

Fan F Yang, Zhao Ren, Wen Zhou, Kejue Jia, Robert Jernigan

{"title":"MODEL-FREE INFERENCE FOR CHARACTERIZING PROTEIN MUTATIONS THROUGH A COEVOLUTIONARY LENS.","authors":"Fan F Yang, Zhao Ren, Wen Zhou, Kejue Jia, Robert Jernigan","doi":"10.1214/26-aoas2145","DOIUrl":"10.1214/26-aoas2145","url":null,"abstract":"Multiple sequence alignment (MSA) data play a crucial role in the study of protein mutations, with contact prediction being a notable application. Existing methods are often model-based or algorithmic and typically do not incorporate statistical inference to quantify the uncertainty of the prediction outcomes. To address this, we propose a novel framework that transforms the task of contact prediction into a statistical testing problem. Our approach is motivated by the partial correlation for continuous random variables. With one-hot encoding of MSA data, we are able to construct a partial correlation graph for multivariate categorical variables. In this framework, two connected nodes in the graph indicate that the corresponding positions on the protein form a contact. A new spectrum-based test statistic is introduced to test whether two positions are partially correlated. Moreover, the new framework enables the identification of amino acid combinations that contribute to the correlation within the identified contacts, an important but largely unexplored aspect of protein mutations. Numerical experiments demonstrate that our proposed method is valid in terms of controlling Type I errors and powerful in general. Real data applications on various protein families further validate the practical utility of our approach in coevolution and mutation analysis.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"20 1","pages":"90-109"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13335161/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148438865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SEMIPARAMETRIC ANALYSIS OF INTERVAL-CENSORED DATA SUBJECT TO INACCURATE DIAGNOSES WITH A TERMINAL EVENT. 具有终末事件的不准确诊断的区间截尾数据的半参数分析。

IF 1.5 4区数学

Annals of Applied Statistics Pub Date : 2026-03-01 Epub Date: 2026-03-20 DOI: 10.1214/25-aoas2134

Yuhao Deng, Donglin Zeng, Yuanjia Wang

{"title":"SEMIPARAMETRIC ANALYSIS OF INTERVAL-CENSORED DATA SUBJECT TO INACCURATE DIAGNOSES WITH A TERMINAL EVENT.","authors":"Yuhao Deng, Donglin Zeng, Yuanjia Wang","doi":"10.1214/25-aoas2134","DOIUrl":"10.1214/25-aoas2134","url":null,"abstract":"Interval-censoring frequently occurs in studies of chronic diseases where disease status is inferred from intermittently collected biomarkers. Although many methods have been developed to analyze such data, they typically assume perfect disease diagnosis, which often does not hold in practice due to the inherent imperfect clinical diagnosis of cognitive functions or measurement errors of biomarkers such as cerebrospinal fluid. In this work, we introduce a semiparametric modeling framework using the Cox proportional hazards model to address interval-censored data in the presence of inaccurate disease diagnosis. Our model incorporates sensitivity and specificity of the diagnosis to account for uncertainty in whether the interval truly contains the disease onset. Furthermore, the framework accommodates scenarios involving a terminal event and when diagnosis is accurate, such as through postmortem analysis. We propose a nonparametric maximum likelihood estimation method for inference and develop an efficient EM algorithm to ensure computational feasibility. The regression coefficient estimators are shown to be asymptotically normal, achieving semiparametric efficiency bounds. We further validate our approach through extensive simulation studies and an application assessing Alzheimer's disease (AD) risk. We find that amyloid-beta is significantly associated with AD, but Tau is predictive of both AD and mortality.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"20 1","pages":"623-640"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13004487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147500496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A general framework for investigating neurodevelopment of brain functional networks using multisite and longitudinal neuroimaging. 使用多位点和纵向神经成像研究脑功能网络神经发育的一般框架。

IF 1.5 4区数学

Annals of Applied Statistics Pub Date : 2026-03-01 Epub Date: 2026-03-20 DOI: 10.1214/25-aoas2133

Joshua Lukemire, Yaotian Wang, Ying Guo

{"title":"A general framework for investigating neurodevelopment of brain functional networks using multisite and longitudinal neuroimaging.","authors":"Joshua Lukemire, Yaotian Wang, Ying Guo","doi":"10.1214/25-aoas2133","DOIUrl":"10.1214/25-aoas2133","url":null,"abstract":"In recent years longitudinal, multi-site imaging studies have emerged as key tools for investigating brain function. These studies follow a large number of participants for an extended period, offering exciting opportunities to uncover brain functional network changes over time as a function of clinical and demographic covariates. However, these studies also introduce many statistical challenges such as site-effects and accounting for the heterogeneous nature of network differences between subjects. Robust statistical methods are highly needed to address these issues, but to date there has been little methods development addressing these problems in the context of data-driven brain network estimation. This work addresses this gap in the literature, introducing a general Bayesian framework, REMBRAiNDT, incorporating site- and subject-effects into the network decomposition, while also enabling covariate effect estimation and efficient information pooling across brain locations. We use our procedure to conduct a novel analysis of neurodevelopment among adolescents in the longitudinal, multi-site ABCD study. We find extensive evidence of increasing functional integration with age in networks associated with higher order cognitive processes. Our study is one of the first to examine neurodevelopment using blind source separation in the longitudinal ABCD study data, and the findings enrich earlier cross-sectional results on neurodevelopment.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"20 1","pages":"604-622"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13008291/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147516374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0