2025-03-24 デラウェア大学(UD)
デラウェア大学の研究者カーティス・ジョンソン准教授とオースティン・ブロックマイヤー助教授は、磁気共鳴エラストグラフィー(MRE)を用いて、脳の硬さが脳年齢の信頼性の高い指標であることを明らかにしました。MREは、MRIスキャン中に頭部に微細な振動を与え、脳組織の硬さをマッピングする技術です。研究では、若年成人の脳は高い硬さを示し、加齢とともに柔らかくなる傾向が観察されました。この手法は、アルツハイマー病などの神経変性疾患の早期診断や、脳の健康状態の評価に役立つ可能性があります。
<関連情報>
- https://www.udel.edu/udaily/2025/march/brain-stiffness-age-dementia-alzheimers-curtis-johnson-austin-brockmeier/
- https://academic.oup.com/biomethods/article/10/1/bpae086/7905461?login=false
MRIを用いた全脳エラストグラフィと体積測定による脳年齢予測 MRI-based whole-brain elastography and volumetric measurements to predict brain age
Claudio Cesar Claros-Olivares, Rebecca G Clements, Grace McIlvain, Curtis L Johnson, Austin J Brockmeier
Biology Methods and Protocols Published:20 November 2024
DOI:https://doi.org/10.1093/biomethods/bpae086
Abstract
Brain age, as a correlate of an individual’s chronological age obtained from structural and functional neuroimaging data, enables assessing developmental or neurodegenerative pathology relative to the overall population. Accurately inferring brain age from brain magnetic resonance imaging (MRI) data requires imaging methods sensitive to tissue health and sophisticated statistical models to identify the underlying age-related brain changes. Magnetic resonance elastography (MRE) is a specialized MRI technique which has emerged as a reliable, non-invasive method to measure the brain’s mechanical properties, such as the viscoelastic shear stiffness and damping ratio. These mechanical properties have been shown to change across the life span, reflect neurodegenerative diseases, and are associated with individual differences in cognitive function. Here, we aim to develop a machine learning framework to accurately predict a healthy individual’s chronological age from maps of brain mechanical properties. This framework can later be applied to understand neurostructural deviations from normal in individuals with neurodevelopmental or neurodegenerative conditions. Using 3D convolutional networks as deep learning models and more traditional statistical models, we relate chronological age as a function of multiple modalities of whole-brain measurements: stiffness, damping ratio, and volume. Evaluations on held-out subjects show that combining stiffness and volume in a multimodal approach achieves the most accurate predictions. Interpretation of the different models highlights important regions that are distinct between the modalities. The results demonstrate the complementary value of MRE measurements in brain age models, which, in future studies, could improve model sensitivity to brain integrity differences in individuals with neuropathology.
