A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility Article Swipe
Robert J. Jirsaraie
,
Aaron J. Gorelik
,
Mārtiņš M. Gataviņš
,
Denis A. Engemann
,
Ryan Bogdan
,
Deanna M. Barch
,
Aristeidis Sotiras
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.patter.2023.100712
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.patter.2023.100712
Brain aging is a complex, multifaceted process that can be challenging to model in ways that are accurate and clinically useful. One of the most common approaches has been to apply machine learning to neuroimaging data with the goal of predicting age in a data-driven manner. Building on initial brain age studies that were derived solely from T1-weighted scans (i.e., unimodal), recent studies have incorporated features across multiple imaging modalities (i.e., "multimodal"). In this systematic review, we show that unimodal and multimodal models have distinct advantages. Multimodal models are the most accurate and sensitive to differences in chronic brain disorders. In contrast, unimodal models from functional magnetic resonance imaging were most sensitive to differences across a broad array of phenotypes. Altogether, multimodal imaging has provided us valuable insight for improving the accuracy of brain age models, but there is still much untapped potential with regard to achieving widespread clinical utility.
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- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.patter.2023.100712
- OA Status
- gold
- Cited By
- 56
- References
- 89
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4365511615
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Raw OpenAlex JSON
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https://openalex.org/W4365511615Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.patter.2023.100712Digital Object Identifier
- Title
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A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utilityWork title
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-01Full publication date if available
- Authors
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Robert J. Jirsaraie, Aaron J. Gorelik, Mārtiņš M. Gataviņš, Denis A. Engemann, Ryan Bogdan, Deanna M. Barch, Aristeidis SotirasList of authors in order
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https://doi.org/10.1016/j.patter.2023.100712Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.patter.2023.100712Direct OA link when available
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Divergence (linguistics), Computer science, Econometrics, Artificial intelligence, Psychology, Mathematics, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
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56Total citation count in OpenAlex
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2025: 19, 2024: 24, 2023: 12, 2022: 1Per-year citation counts (last 5 years)
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89Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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