Neda Sadeghi, Isabelle F van der Velpen, Bradley T Baker, Ishaan Batta, Kyle J Cahill, Sarah Genon, Ethan McCormick, Léa C Michel, Dustin Moraczewski, Masoud Seraji, Philip Shaw, Rogers F Silva, Najme Soleimani, Emma Sprooten, Øystein Sørensen, Adam G Thomas, Audrey Thurm, Zi-Xuan Zhou, Vince D Calhoun, Rogier Kievit, Anna Plachti, Xi-Nian Zuo, Tonya White
{"title":"The interplay between brain and behavior during development: A multisite effort to generate and share simulated datasets.","authors":"Neda Sadeghi, Isabelle F van der Velpen, Bradley T Baker, Ishaan Batta, Kyle J Cahill, Sarah Genon, Ethan McCormick, Léa C Michel, Dustin Moraczewski, Masoud Seraji, Philip Shaw, Rogers F Silva, Najme Soleimani, Emma Sprooten, Øystein Sørensen, Adam G Thomas, Audrey Thurm, Zi-Xuan Zhou, Vince D Calhoun, Rogier Kievit, Anna Plachti, Xi-Nian Zuo, Tonya White","doi":"10.1038/s41597-025-04740-3","DOIUrl":null,"url":null,"abstract":"<p><p>One of the challenges in the field of neuroimaging is that we often lack knowledge about the underlying truth and whether our methods can detect developmental changes. To address this gap, five research groups around the globe created simulated datasets embedded with their assumptions of the interplay between brain development, cognition, and behavior. Each group independently created the datasets, unaware of the approaches and assumptions made by the other groups. Each group simulated three datasets with the same variables, each with 10,000 participants over 7 longitudinal waves, ranging from 7 to 20 years-of-age. The independently created datasets include demographic data, brain derived variables along with behavior and cognition variables. These datasets and code that were used to generate the datasets can be downloaded and used by the research community to apply different longitudinal models to determine the underlying patterns and assumptions where the ground truth is known.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"473"},"PeriodicalIF":5.8000,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11928570/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-04740-3","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
One of the challenges in the field of neuroimaging is that we often lack knowledge about the underlying truth and whether our methods can detect developmental changes. To address this gap, five research groups around the globe created simulated datasets embedded with their assumptions of the interplay between brain development, cognition, and behavior. Each group independently created the datasets, unaware of the approaches and assumptions made by the other groups. Each group simulated three datasets with the same variables, each with 10,000 participants over 7 longitudinal waves, ranging from 7 to 20 years-of-age. The independently created datasets include demographic data, brain derived variables along with behavior and cognition variables. These datasets and code that were used to generate the datasets can be downloaded and used by the research community to apply different longitudinal models to determine the underlying patterns and assumptions where the ground truth is known.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.