Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age.
The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance image (MRI) into an effective "brain age" metric can provide a holistic, individualized, and objective view of how the brain interacts with various factors (e.g., genetics and lifestyle) during aging. Brain...
| Publicado en: | Frontiers in Aging Neuroscience pp. 1 - 14 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Frontiers Media S.A.
2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173647873&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173647873 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 2023 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 173647873 173647873 173647873 10.3389/fnagi.2023.1249415 173647873 ppf: 1 ppct: 13 formats: tig: atl: Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age. aug: au: Sheng He Yi Guan Chia Hsin Cheng Moore, Tara L. Luebke, Jennifer I. Killiany, Ronald J. Rosene, Douglas L. Koo, Bang-Bon Yangming Ou affil: Harvard Medical School, Boston Children's Hospital, Boston, MA, United States sug: subj: Artificial Intelligence Magnetic Resonance Imaging Brain Mapping Frontal Lobe Anatomy and Histology White Matter Anatomy and Histology Animal Studies Primates Male Female Models, Biological Brain Physiology Aging Human Longevity Sample Size Phenotype Descriptive Statistics Comparative Studies Life Style Funding Source Male Female ab: The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance image (MRI) into an effective "brain age" metric can provide a holistic, individualized, and objective view of how the brain interacts with various factors (e.g., genetics and lifestyle) during aging. Brain age predictions using deep learning (DL) have been widely used to quantify the developmental status of human brains, but their wider application to serve biomedical purposes is under criticism for requiring large samples and complicated interpretability. Animal models, i.e., rhesus monkeys, have offered a unique lens to understand the human brain - being a species in which aging patterns are similar, for which environmental and lifestyle factors are more readily controlled. However, applying DL methods in animal models suffers from data insufficiency as the availability of animal brain MRIs is limited compared to many thousands of human MRIs. We showed that transfer learning can mitigate the sample size problem, where transferring the pre-trained AI models from 8,859 human brain MRIs improved monkey brain age estimation accuracy and stability. The highest accuracy and stability occurred when transferring the 3D ResNet [mean absolute error (MAE) = 1.83 years] and the 2D global-local transformer (MAE = 1.92 years) models. Our models identified the frontal white matter as the most important feature for monkey brain age predictions, which is consistent with previous histological findings. This first DL-based, anatomically interpretable, and adaptive brain age estimator could broaden the application of AI techniques to various animal or disease samples and widen opportunities for research in non-human primate brains across the lifespan. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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