Cassie Ann Short , Andrea Hildebrandt , Robin Bosse , Stefan Debener , Metin Özyağcılar , Katharina Paul , Jan Wacker , Daniel Kristanto
{"title":"迷失在大脑电图多重宇宙中?比较代表性管道选择的抽样方法。","authors":"Cassie Ann Short , Andrea Hildebrandt , Robin Bosse , Stefan Debener , Metin Özyağcılar , Katharina Paul , Jan Wacker , Daniel Kristanto","doi":"10.1016/j.jneumeth.2025.110564","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>The multiplicity of defensible pipelines for processing and analysing data has been implicated as a core contributor to low replicability, creating uncertainty about the robustness of results to defensible variations. This is exacerbated where many defensible pipelines exist, such as in processing electroencephalography (EEG) signals. In multiverse analyses, equally defensible pipelines are computed and the robustness across pipelines is reported. Computing all pipelines is often infeasible, and researchers rely on sampling approaches, assuming representativeness of the full multiverse. However, different sampling methods may yield different robustness estimates, introducing what we term <em>multiverse sampling uncertainty</em>.</div></div><div><h3>New method</h3><div>We developed an open-source tool to compare pipeline samples on their representativeness of the full multiverse. We computed a 528-pipeline use case multiverse on EEG recordings during an emotion classification task to predict extraversion scores from the Late Positive Potential. We applied three sampling methods (random, stratified, active learning) to sample 26 pipelines (5 %) and evaluated the representativeness of model fit distributions.</div></div><div><h3>Results</h3><div>Our results highlight variability in the representativeness of model fit distributions across samples, with active learning and stratified sampling most closely representing the full multiverse. Replicability of results is reported using cross-validation, and reproducibility is explored across pipeline sample sizes.</div></div><div><h3>Comparison with existing methods</h3><div>Large multiverse analyses in neuroimaging typically rely on sampling, but sampling approaches are not often systematically compared for their representation of the full multiverse.</div></div><div><h3>Conclusions</h3><div>The need for representative pipeline sampling to mitigate bias in large multiverse analyses is discussed.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110564"},"PeriodicalIF":2.3000,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lost in a large EEG multiverse? Comparing sampling approaches for representative pipeline selection\",\"authors\":\"Cassie Ann Short , Andrea Hildebrandt , Robin Bosse , Stefan Debener , Metin Özyağcılar , Katharina Paul , Jan Wacker , Daniel Kristanto\",\"doi\":\"10.1016/j.jneumeth.2025.110564\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>The multiplicity of defensible pipelines for processing and analysing data has been implicated as a core contributor to low replicability, creating uncertainty about the robustness of results to defensible variations. This is exacerbated where many defensible pipelines exist, such as in processing electroencephalography (EEG) signals. In multiverse analyses, equally defensible pipelines are computed and the robustness across pipelines is reported. Computing all pipelines is often infeasible, and researchers rely on sampling approaches, assuming representativeness of the full multiverse. However, different sampling methods may yield different robustness estimates, introducing what we term <em>multiverse sampling uncertainty</em>.</div></div><div><h3>New method</h3><div>We developed an open-source tool to compare pipeline samples on their representativeness of the full multiverse. We computed a 528-pipeline use case multiverse on EEG recordings during an emotion classification task to predict extraversion scores from the Late Positive Potential. We applied three sampling methods (random, stratified, active learning) to sample 26 pipelines (5 %) and evaluated the representativeness of model fit distributions.</div></div><div><h3>Results</h3><div>Our results highlight variability in the representativeness of model fit distributions across samples, with active learning and stratified sampling most closely representing the full multiverse. Replicability of results is reported using cross-validation, and reproducibility is explored across pipeline sample sizes.</div></div><div><h3>Comparison with existing methods</h3><div>Large multiverse analyses in neuroimaging typically rely on sampling, but sampling approaches are not often systematically compared for their representation of the full multiverse.</div></div><div><h3>Conclusions</h3><div>The need for representative pipeline sampling to mitigate bias in large multiverse analyses is discussed.</div></div>\",\"PeriodicalId\":16415,\"journal\":{\"name\":\"Journal of Neuroscience Methods\",\"volume\":\"424 \",\"pages\":\"Article 110564\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Neuroscience Methods\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0165027025002080\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Neuroscience Methods","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165027025002080","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Lost in a large EEG multiverse? Comparing sampling approaches for representative pipeline selection
Background
The multiplicity of defensible pipelines for processing and analysing data has been implicated as a core contributor to low replicability, creating uncertainty about the robustness of results to defensible variations. This is exacerbated where many defensible pipelines exist, such as in processing electroencephalography (EEG) signals. In multiverse analyses, equally defensible pipelines are computed and the robustness across pipelines is reported. Computing all pipelines is often infeasible, and researchers rely on sampling approaches, assuming representativeness of the full multiverse. However, different sampling methods may yield different robustness estimates, introducing what we term multiverse sampling uncertainty.
New method
We developed an open-source tool to compare pipeline samples on their representativeness of the full multiverse. We computed a 528-pipeline use case multiverse on EEG recordings during an emotion classification task to predict extraversion scores from the Late Positive Potential. We applied three sampling methods (random, stratified, active learning) to sample 26 pipelines (5 %) and evaluated the representativeness of model fit distributions.
Results
Our results highlight variability in the representativeness of model fit distributions across samples, with active learning and stratified sampling most closely representing the full multiverse. Replicability of results is reported using cross-validation, and reproducibility is explored across pipeline sample sizes.
Comparison with existing methods
Large multiverse analyses in neuroimaging typically rely on sampling, but sampling approaches are not often systematically compared for their representation of the full multiverse.
Conclusions
The need for representative pipeline sampling to mitigate bias in large multiverse analyses is discussed.
期刊介绍:
The Journal of Neuroscience Methods publishes papers that describe new methods that are specifically for neuroscience research conducted in invertebrates, vertebrates or in man. Major methodological improvements or important refinements of established neuroscience methods are also considered for publication. The Journal''s Scope includes all aspects of contemporary neuroscience research, including anatomical, behavioural, biochemical, cellular, computational, molecular, invasive and non-invasive imaging, optogenetic, and physiological research investigations.