Age prediction using resting-state functional MRI Article Swipe
Jose Ramon Chang
,
Zai‐Fu Yao
,
Shulan Hsieh
,
Torbjörn E. M. Nordling
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.12.26.23300530
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.12.26.23300530
The increasing lifespan and large individual differences in cognitive capability highlight the importance of comprehending the aging process of the brain. Contrary to visible signs of bodily ageing, like greying of hair and loss of muscle mass, the internal changes that occur within our brains remain less apparent until they impair function. Brain age, distinct from chronological age, reflects our brain’s health status and may deviate from our actual chronological age. Notably, brain age has been associated with mortality and depression. The brain is plastic and can compensate even for severe structural damage by rewiring. Functional characterization offers insights that structural cannot provide. Contrary to the multitude of studies relying on structural magnetic resonance imaging (MRI), we utilize resting-state functional MRI (rsfMRI). We also address the issue of inclusion of subjects with abnormal brain ageing through outlier removal. In this study, we employ the Least Absolute Shrinkage and Selection Operator (LASSO) to identify the 39 most predictive correlations derived from the rsfMRI data. The data is from a cohort of 116 healthy right-handed volunteers, aged 18-18 years (9 81 male female, mean age 8, SD 11) collected at the Mind Research Imaging Center at the National Cheng Kung University. We establish a normal reference model by excluding 68 outliers, which achieves a leave-one-out mean absolute error of 2. 8 years. By asking which additional features that are needed to predict the chronological age of the outliers with a smaller error, we identify correlations predictive of abnormal aging. These are associated with the Default Mode Network (DMN). Our normal reference model has the lowest prediction error among published models evaluated on adult subjects of almost all ages and is thus a candidate for screening for abnormal brain aging that has not yet manifested in cognitive decline. This study advances our ability to predict brain aging and provides insights into potential biomarkers for assessing brain age, suggesting that the role of DMN in brain aging should be studied further.
Related Topics
Concepts
Outlier
Resting state fMRI
Ageing
Lasso (programming language)
Cohort
Magnetic resonance imaging
Psychology
Neuroimaging
Functional magnetic resonance imaging
Cognition
Brain aging
Gerontology
Medicine
Audiology
Neuroscience
Internal medicine
Artificial intelligence
Computer science
Radiology
World Wide Web
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.12.26.23300530
- https://www.medrxiv.org/content/medrxiv/early/2023/12/28/2023.12.26.23300530.full.pdf
- OA Status
- green
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390322608
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390322608Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.12.26.23300530Digital Object Identifier
- Title
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Age prediction using resting-state functional MRIWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-12-28Full publication date if available
- Authors
-
Jose Ramon Chang, Zai‐Fu Yao, Shulan Hsieh, Torbjörn E. M. NordlingList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.12.26.23300530Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2023/12/28/2023.12.26.23300530.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://www.medrxiv.org/content/medrxiv/early/2023/12/28/2023.12.26.23300530.full.pdfDirect OA link when available
- Concepts
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Outlier, Resting state fMRI, Ageing, Lasso (programming language), Cohort, Magnetic resonance imaging, Psychology, Neuroimaging, Functional magnetic resonance imaging, Cognition, Brain aging, Gerontology, Medicine, Audiology, Neuroscience, Internal medicine, Artificial intelligence, Computer science, Radiology, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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49Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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