[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124054-en":3,"doc-seo-124054-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124054,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","A Machine Learning Study of Anxiety-related Symptoms and Error-related Brain Activity","Changes in error processing can be observed across a range of anxiety-related disorders, yet prior studies report contradictions and poor replicability, leaving the mapping between brain error signals and specific anxiety symptoms unclear. This study collected 16 self-reported scores of anxiety dimensions and extracted spatial EEG features from 171 participants. Machine learning identified symptoms central to elevated ERN/Pe and assessed how generalizable traditional statistics are. ERN related to rumination, threat overestimation, and inhibitory intolerance of uncertainty; Pe related to rumination, prospective intolerance of uncertainty, and behavioral inhibition. Results indicate that multiple EEG variance sources encode individual differences in error processing and that validation methods should be updated to support robust, clinically useful biomarkers.","A Machine Learning Study of Anxiety-related Symptoms  \nand Error-related Brain Activity  \nAnna Grabowska, Filip Sondej, and Magdalena Senderecka  \nAbstract  \n■ Changes in error processing are observable in a range of anxiety-related disorders. Numerous studies, however, have reported contradictory and nonreplicating findings, thus the exact mapping of brain response to errors (i.e., error-related negativity [ERN]; error-related positivity [Pe]) onto specific anxiety symptoms remains unclear. In this study, we collected 16 self-reported scores of anxiety dimensions and obtained spatial features of EEG recordings from 171 individuals. We then used machine learning to (1) identify symptoms that are central for elevated ERN/Pe and (2) estimate the generalizability of traditional statistical approaches. ERN was associated with  \nrumination, threat overestimation, and inhibitory intolerance of uncertainty. Pe was associated with rumination, prospective intolerance of uncertainty, and behavioral inhibition. Our findings emphasize that not only the amplitude of ERN but also other sources of brain signal variance encode information relevant to individual differences in error processing. The results of the generalizability check reveal the need for a change in resultvalidation methods to move toward robust findings that reflectstable individual differences and clinically useful biomarkers. Our study benefits from the use of machine learning to improve the generalizability of results.  \n■  \nINTRODUCTION  \nThe primary goal of research in cognitive and affective neuroscience is to establish the relationship between the structure and activity of the brain and behavior. Although this brain–behavior association is the focus of most studies in cognitive neurophysiology, the reliability of reported results has recently come under criticism (Brederoo, Nieuwenstein, Cornelissen, & Lorist, 2019) . Standard statistical analysis usually includes an examination of differences between either groups or conditions, and results are often described in terms of the mean, the standard deviation, and the confidence intervals for each group or condition (Calhoun, Lawrie, Mourao-Miranda, & Stephan, 2017) . However, making inferences at the group level can lead to misleading conclusions. As suggested by Rouder and colleagues (2021) in their recent study, inferring from the mean makes sense only when all individuals in a population show an effect in the same direction. In such a case, an effect could be both explained and expressed as a function of the variables of interest; the same function can then be used to predict the effect for all individuals. The picture is more complicated when individuals in the population show an effect in the opposite directions or part of the population shows no effect. Such complex individual differences suggest that the explained phenomenon is more complex than would appear from a group study, therefore attempting to analyze and predict this phenomenon  \nJagiellonian University, Kraków, Poland  \nthrough group comparison or simple linear regression may yield misleading results.  \nMoreover, recent controversies about the level of replicability in behavioral research have attracted researchers’interest to the impact of various methodological and nonmethodological choices on obtained results (Algermissen et al., 2022; Pavlov et al., 2021). Studies differ, for example, in their specifications of neuronal biomarkers (Clayson, 2020), their specifications of experimental paradigms (Weinberg, Dieterich, & Riesel, 2015), or their sample characteristics (Ging-Jehli, Ratcliff, & Arnold, 2021) . All these factors contribute to differences in the results of apparently similar studies and create almost insurmountable barriers to establishing robust and stable brain– behavior associations. These fundamental problems have begun to be addressed by means of preregistrations of studies and increased rigor in describing both methods and samples; neve","cbCaibY168TUusCJ","https://ap.wps.com/l/cbCaibY168TUusCJ","pdf",1998122,1,26,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why is the relationship between error signals and anxiety symptoms still unclear?\",\"answer\":\"Many earlier studies report contradictory and nonreplicating findings, so the mapping of brain error responses (ERN/Pe) to specific anxiety symptoms is not well established.\"},{\"question\":\"What data and features were used in the machine learning analysis?\",\"answer\":\"The study used 16 self-reported anxiety dimension scores and spatial features extracted from EEG recordings of 171 individuals.\"},{\"question\":\"How did ERN and Pe relate to different anxiety-related processes?\",\"answer\":\"ERN was associated with rumination, threat overestimation, and inhibitory intolerance of uncertainty, while Pe was associated with rumination, prospective intolerance of uncertainty, and behavioral inhibition.\"}]","A Machine Learning Study of Anxiety-related Symptoms and Error-related Brain Activity | 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is the relationship between error signals and anxiety symptoms still unclear?","Question",{"text":76,"@type":77},"Many earlier studies report contradictory and nonreplicating findings, so the mapping of brain error responses (ERN/Pe) to specific anxiety symptoms is not well established.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data and features were used in the machine learning analysis?",{"text":81,"@type":77},"The study used 16 self-reported anxiety dimension scores and spatial features extracted from EEG recordings of 171 individuals.",{"name":83,"@type":74,"acceptedAnswer":84},"How did ERN and Pe relate to different anxiety-related processes?",{"text":85,"@type":77},"ERN was associated with rumination, threat overestimation, and inhibitory intolerance of uncertainty, while Pe was associated with rumination, prospective intolerance of uncertainty, and behavioral 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