Current methods and limitations for longitudinal fMRI analysis across development
The human brain is remarkably plastic. The brain changes dramatically across development, with ongoing functional development continuing well into the third decade of life and substantial changes occurring again in older age. Dynamic changes in brain function are thought to underlie the innumerable...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2018-10-01
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| Series: | Developmental Cognitive Neuroscience |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1878929317300713 |
| _version_ | 1818339586937454592 |
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| author | Tara Madhyastha Matthew Peverill Natalie Koh Connor McCabe John Flournoy Kate Mills Kevin King Jennifer Pfeifer Katie A. McLaughlin |
| author_facet | Tara Madhyastha Matthew Peverill Natalie Koh Connor McCabe John Flournoy Kate Mills Kevin King Jennifer Pfeifer Katie A. McLaughlin |
| author_sort | Tara Madhyastha |
| collection | DOAJ |
| description | The human brain is remarkably plastic. The brain changes dramatically across development, with ongoing functional development continuing well into the third decade of life and substantial changes occurring again in older age. Dynamic changes in brain function are thought to underlie the innumerable changes in cognition, emotion, and behavior that occur across development. The brain also changes in response to experience, which raises important questions about how the environment influences the developing brain. Longitudinal functional magnetic resonance imaging (fMRI) studies are an essential means of understanding these developmental changes and their cognitive, emotional, and behavioral correlates. This paper provides an overview of common statistical models of longitudinal change applicable to developmental cognitive neuroscience, and a review of the functionality provided by major software packages for longitudinal fMRI analysis. We demonstrate that there are important developmental questions that cannot be answered using available software. We propose alternative approaches for addressing problems that are commonly faced in modeling developmental change with fMRI data. Keywords: Longitudinal modeling, Functional magnetic resonance imaging (fMRI), General Linear Model, Structural Equation Modeling, Developmental change |
| first_indexed | 2024-12-13T15:29:22Z |
| format | Article |
| id | doaj.art-e2e50d2609634af39bcdfc2b261bfdb9 |
| institution | Directory Open Access Journal |
| issn | 1878-9293 |
| language | English |
| last_indexed | 2024-12-13T15:29:22Z |
| publishDate | 2018-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Developmental Cognitive Neuroscience |
| spelling | doaj.art-e2e50d2609634af39bcdfc2b261bfdb92022-12-21T23:40:15ZengElsevierDevelopmental Cognitive Neuroscience1878-92932018-10-0133118128Current methods and limitations for longitudinal fMRI analysis across developmentTara Madhyastha0Matthew Peverill1Natalie Koh2Connor McCabe3John Flournoy4Kate Mills5Kevin King6Jennifer Pfeifer7Katie A. McLaughlin8Radiology, University of Washington, United States; Corresponding author.Psychology, University of Washington, United StatesRadiology, University of Washington, United StatesPsychology, University of Washington, United StatesPsychology, University of Oregon, United StatesPsychology, University of Oregon, United StatesPsychology, University of Washington, United StatesPsychology, University of Oregon, United StatesPsychology, University of Washington, United StatesThe human brain is remarkably plastic. The brain changes dramatically across development, with ongoing functional development continuing well into the third decade of life and substantial changes occurring again in older age. Dynamic changes in brain function are thought to underlie the innumerable changes in cognition, emotion, and behavior that occur across development. The brain also changes in response to experience, which raises important questions about how the environment influences the developing brain. Longitudinal functional magnetic resonance imaging (fMRI) studies are an essential means of understanding these developmental changes and their cognitive, emotional, and behavioral correlates. This paper provides an overview of common statistical models of longitudinal change applicable to developmental cognitive neuroscience, and a review of the functionality provided by major software packages for longitudinal fMRI analysis. We demonstrate that there are important developmental questions that cannot be answered using available software. We propose alternative approaches for addressing problems that are commonly faced in modeling developmental change with fMRI data. Keywords: Longitudinal modeling, Functional magnetic resonance imaging (fMRI), General Linear Model, Structural Equation Modeling, Developmental changehttp://www.sciencedirect.com/science/article/pii/S1878929317300713 |
| spellingShingle | Tara Madhyastha Matthew Peverill Natalie Koh Connor McCabe John Flournoy Kate Mills Kevin King Jennifer Pfeifer Katie A. McLaughlin Current methods and limitations for longitudinal fMRI analysis across development Developmental Cognitive Neuroscience |
| title | Current methods and limitations for longitudinal fMRI analysis across development |
| title_full | Current methods and limitations for longitudinal fMRI analysis across development |
| title_fullStr | Current methods and limitations for longitudinal fMRI analysis across development |
| title_full_unstemmed | Current methods and limitations for longitudinal fMRI analysis across development |
| title_short | Current methods and limitations for longitudinal fMRI analysis across development |
| title_sort | current methods and limitations for longitudinal fmri analysis across development |
| url | http://www.sciencedirect.com/science/article/pii/S1878929317300713 |
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