Statistics and data science in imaging最新文献

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A Novel, Variance Component-Based Method for Detecting Brain-Behavior Associations in Neuroimaging Data. 一种新的基于方差分量的神经成像数据脑行为关联检测方法。
Statistics and data science in imaging Pub Date : 2025-01-01 Epub Date: 2025-12-17 DOI: 10.1080/29979676.2025.2579919
Christina E Chen, Jeremy Rubin, Lior Rennert, Mackenzie Edmondson, Simon N Vandekar, Russell T Shinohara
{"title":"A Novel, Variance Component-Based Method for Detecting Brain-Behavior Associations in Neuroimaging Data.","authors":"Christina E Chen, Jeremy Rubin, Lior Rennert, Mackenzie Edmondson, Simon N Vandekar, Russell T Shinohara","doi":"10.1080/29979676.2025.2579919","DOIUrl":"10.1080/29979676.2025.2579919","url":null,"abstract":"<p><p>The sequence kernel association test (SKAT) is a widely used and computationally efficient method in high-dimensional studies that tests for the joint effect of multiple predictors while accommodating covariates. However, the omnibus nature of the test hinders interpretation. We develop a new method called LaxKAT (linear maximum kernel association test) that can identify both global and local signal in high-dimensional data. The LaxKAT statistic maximizes the SKAT statistic over a pre-specified subspace of linear kernels. We demonstrate via simulations that it exhibits improved global and local power compared to previous methods while controlling the family-wise error rate (FWER). We also apply LaxKAT to neuroimaging data from cognitively normal controls in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to identify brain regions exhibiting sex-specific differences in cortical thickness patterns.</p>","PeriodicalId":520439,"journal":{"name":"Statistics and data science in imaging","volume":"2 1","pages":"2579919"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12826657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146055901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Benchmarking Spatial Co-Localization Methods for Single-Cell Multiplex Imaging Data with Applications to High-Grade Serous Ovarian and Triple Negative Breast Cancer. 单细胞多重成像数据的基准空间共定位方法在高级别浆液性卵巢癌和三阴性乳腺癌中的应用。
Statistics and data science in imaging Pub Date : 2025-01-01 Epub Date: 2025-01-15 DOI: 10.1080/29979676.2024.2437947
Alex C Soupir, Ishaan V Gadiyar, Bryan R Helm, Coleman R Harris, Simon N Vandekar, Lauren C Peres, Robert J Coffey, Julia Wrobel, Siyuan Ma, Brooke L Fridley
{"title":"Benchmarking Spatial Co-Localization Methods for Single-Cell Multiplex Imaging Data with Applications to High-Grade Serous Ovarian and Triple Negative Breast Cancer.","authors":"Alex C Soupir, Ishaan V Gadiyar, Bryan R Helm, Coleman R Harris, Simon N Vandekar, Lauren C Peres, Robert J Coffey, Julia Wrobel, Siyuan Ma, Brooke L Fridley","doi":"10.1080/29979676.2024.2437947","DOIUrl":"10.1080/29979676.2024.2437947","url":null,"abstract":"<p><p>Single-cell multiplex imaging (scMI) measures cell locations and phenotypes within a tissue and can be used to understand the tumor microenvironment. In scMI studies, it is often of interest to quantify spatial co-localization of immune cells and its association with clinical outcomes; however, it remains unknown which of the many available spatial indices have adequate power to detect spatial within-sample co-localization and its association with patient outcomes, such as survival. In this study, the performance of six frequentist metrics of spatial co-localization used in scMI studies were evaluated. Simulated data was used to assess the power and type I error of these spatial metrics to detect signficant co-localization. Furthermore, these spatial co-localization methods were applied to two scMI studies - a high-grade serous ovarian cancer (HGSOC) study and triple negative breast cancer (TNBC) study - to detect within-sample co-localization between cell types and their sensitivity to detect differences in survival across samples. In the simulation study, Ripley's <i>K</i> had the greatest power to identify co-localization followed closely by pair correlation <i>g</i>; all other statistics showed little power across all simulation scenarios. In the application of the methods to cancer studies, the results consistently point to pair correlation <i>g</i> and Ripley's <i>K</i> as indices with the most power for detecting significant co-localization in scMI data. Furthermore, pair correlation <i>g</i>, Ripley's <i>K</i>, and the scLMM index were most effective for estimating between-sample associations between level of co-localization and survival.</p>","PeriodicalId":520439,"journal":{"name":"Statistics and data science in imaging","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11883755/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Covariate Adjusted Functional Mixed Membership Models. 协变量调整的功能混合隶属模型。
Statistics and data science in imaging Pub Date : 2025-01-01 Epub Date: 2025-10-30 DOI: 10.1080/29979676.2025.2566646
Nicholas Marco, Damla Şentürk, Shafali Jeste, Charlotte DiStefano, Abigail Dickinson, Donatello Telesca
{"title":"Covariate Adjusted Functional Mixed Membership Models.","authors":"Nicholas Marco, Damla Şentürk, Shafali Jeste, Charlotte DiStefano, Abigail Dickinson, Donatello Telesca","doi":"10.1080/29979676.2025.2566646","DOIUrl":"10.1080/29979676.2025.2566646","url":null,"abstract":"<p><p>Mixed membership models are a flexible class of models used for unsupervised learning that allow each observation to partially belong to multiple clusters or features. In this article, we extend the framework of functional mixed membership models to allow for covariate-dependent modeling structures. The framework uses a multivariate Karhunen-Loève decomposition, which allows for a scalable and flexible model. Within this framework, we establish a set of sufficient conditions to ensure the identifiability of the mean, covariance, and allocation structure up to a permutation of the labels. This article is primarily motivated by studies on functional brain imaging through electroencephalography (EEG) of children with autism spectrum disorder (ASD). Using the proposed framework, we provide novel insight into the heterogeneity of developmental changes in alpha oscillations and show that individuals with ASD have smaller developmental changes compared to their typically developing counterparts.</p>","PeriodicalId":520439,"journal":{"name":"Statistics and data science in imaging","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12610336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145515304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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