{"title":"Unveiling Schizophrenia: a study with generalized functional linear mixed model via the investigation of functional random effects.","authors":"Rongxiang Rui, Wei Xiong, Jianxin Pan, Maozai Tian","doi":"10.1093/biostatistics/kxae049","DOIUrl":null,"url":null,"abstract":"<p><p>Previous studies have identified attenuated pre-speech activity and speech sound suppression in individuals with Schizophrenia, with similar patterns observed in basic tasks entailing button-pressing to perceive a tone. However, it remains unclear whether these patterns are uniform across individuals or vary from person to person. Motivated by electroencephalographic (EEG) data from a Schizophrenia study, we develop a generalized functional linear mixed model (GFLMM) for repeated measurements by incorporating subject-specific functional random effects associated with multiple functional predictors. To assess the significance of these functional effects, we employ two different multivariate functional principal component analysis methods, which transform the GFLMM into a conventional generalized linear mixed model, thereby facilitating its implementation with standard software. Furthermore, we introduce a cutting-edge testing approach utilizing working responses to detect both subject-specific and predictor-specific functional random effects. Monte Carlo simulation studies demonstrate the effectiveness of our proposed testing method. Application of the proposed methods to the Schizophrenia data reveals significant subject-specific effects of human brain activity in the frontal zone (Fz) and the central zone (Cz), providing valuable insights into the potential variations among individuals, from healthy controls to those diagnosed with Schizophrenia.</p>","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biostatistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1093/biostatistics/kxae049","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Previous studies have identified attenuated pre-speech activity and speech sound suppression in individuals with Schizophrenia, with similar patterns observed in basic tasks entailing button-pressing to perceive a tone. However, it remains unclear whether these patterns are uniform across individuals or vary from person to person. Motivated by electroencephalographic (EEG) data from a Schizophrenia study, we develop a generalized functional linear mixed model (GFLMM) for repeated measurements by incorporating subject-specific functional random effects associated with multiple functional predictors. To assess the significance of these functional effects, we employ two different multivariate functional principal component analysis methods, which transform the GFLMM into a conventional generalized linear mixed model, thereby facilitating its implementation with standard software. Furthermore, we introduce a cutting-edge testing approach utilizing working responses to detect both subject-specific and predictor-specific functional random effects. Monte Carlo simulation studies demonstrate the effectiveness of our proposed testing method. Application of the proposed methods to the Schizophrenia data reveals significant subject-specific effects of human brain activity in the frontal zone (Fz) and the central zone (Cz), providing valuable insights into the potential variations among individuals, from healthy controls to those diagnosed with Schizophrenia.
期刊介绍:
Among the important scientific developments of the 20th century is the explosive growth in statistical reasoning and methods for application to studies of human health. Examples include developments in likelihood methods for inference, epidemiologic statistics, clinical trials, survival analysis, and statistical genetics. Substantive problems in public health and biomedical research have fueled the development of statistical methods, which in turn have improved our ability to draw valid inferences from data. The objective of Biostatistics is to advance statistical science and its application to problems of human health and disease, with the ultimate goal of advancing the public''s health.