Enhancing causal inference in population-based neuroimaging data in children and adolescents
Recent years have seen the increasing availability of large, population-based, longitudinal neuroimaging datasets, providing unprecedented capacity to examine brain-behavior relationships in the neurodevelopmental context. However, the ability of these datasets to deliver causal insights into brain-...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Developmental Cognitive Neuroscience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1878929324001269 |
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| author | Rachel Visontay Lindsay M. Squeglia Matthew Sunderland Emma K. Devine Hollie Byrne Louise Mewton |
| author_facet | Rachel Visontay Lindsay M. Squeglia Matthew Sunderland Emma K. Devine Hollie Byrne Louise Mewton |
| author_sort | Rachel Visontay |
| collection | DOAJ |
| description | Recent years have seen the increasing availability of large, population-based, longitudinal neuroimaging datasets, providing unprecedented capacity to examine brain-behavior relationships in the neurodevelopmental context. However, the ability of these datasets to deliver causal insights into brain-behavior relationships relies on the application of purpose-built analysis methods to counter the biases that otherwise preclude causal inference from observational data. Here we introduce these approaches (i.e., propensity score-based methods, the ‘G-methods’, targeted maximum likelihood estimation, and causal mediation analysis) and conduct a review to determine the extent to which they have been applied thus far in the field of developmental cognitive neuroscience. We identify just eight relevant studies, most of which employ propensity score-based methods. Many approaches are entirely absent from the literature, particularly those that promote causal inference in settings with complex, multi-wave data and repeated neuroimaging assessments. Causality is central to an etiological understanding of the relationship between the brain and behavior, as well as for identifying targets for prevention and intervention. Careful application of methods for causal inference may help the field of developmental cognitive neuroscience approach these goals. |
| format | Article |
| id | doaj-art-cddb7401e8044a86a8c96f30c7b82d80 |
| institution | DOAJ |
| issn | 1878-9293 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Developmental Cognitive Neuroscience |
| spelling | doaj-art-cddb7401e8044a86a8c96f30c7b82d802025-08-20T02:48:58ZengElsevierDevelopmental Cognitive Neuroscience1878-92932024-12-017010146510.1016/j.dcn.2024.101465Enhancing causal inference in population-based neuroimaging data in children and adolescentsRachel Visontay0Lindsay M. Squeglia1Matthew Sunderland2Emma K. Devine3Hollie Byrne4Louise Mewton5The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, Australia; Corresponding author.Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, USAThe Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, AustraliaThe Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, AustraliaThe Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, AustraliaThe Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, AustraliaRecent years have seen the increasing availability of large, population-based, longitudinal neuroimaging datasets, providing unprecedented capacity to examine brain-behavior relationships in the neurodevelopmental context. However, the ability of these datasets to deliver causal insights into brain-behavior relationships relies on the application of purpose-built analysis methods to counter the biases that otherwise preclude causal inference from observational data. Here we introduce these approaches (i.e., propensity score-based methods, the ‘G-methods’, targeted maximum likelihood estimation, and causal mediation analysis) and conduct a review to determine the extent to which they have been applied thus far in the field of developmental cognitive neuroscience. We identify just eight relevant studies, most of which employ propensity score-based methods. Many approaches are entirely absent from the literature, particularly those that promote causal inference in settings with complex, multi-wave data and repeated neuroimaging assessments. Causality is central to an etiological understanding of the relationship between the brain and behavior, as well as for identifying targets for prevention and intervention. Careful application of methods for causal inference may help the field of developmental cognitive neuroscience approach these goals.http://www.sciencedirect.com/science/article/pii/S1878929324001269CausalityCausal inferencePropensity scoresNeuroimagingChildrenAdolescents |
| spellingShingle | Rachel Visontay Lindsay M. Squeglia Matthew Sunderland Emma K. Devine Hollie Byrne Louise Mewton Enhancing causal inference in population-based neuroimaging data in children and adolescents Developmental Cognitive Neuroscience Causality Causal inference Propensity scores Neuroimaging Children Adolescents |
| title | Enhancing causal inference in population-based neuroimaging data in children and adolescents |
| title_full | Enhancing causal inference in population-based neuroimaging data in children and adolescents |
| title_fullStr | Enhancing causal inference in population-based neuroimaging data in children and adolescents |
| title_full_unstemmed | Enhancing causal inference in population-based neuroimaging data in children and adolescents |
| title_short | Enhancing causal inference in population-based neuroimaging data in children and adolescents |
| title_sort | enhancing causal inference in population based neuroimaging data in children and adolescents |
| topic | Causality Causal inference Propensity scores Neuroimaging Children Adolescents |
| url | http://www.sciencedirect.com/science/article/pii/S1878929324001269 |
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