
高齢者の認知機能低下を捉えるバイオマーカーとしての「脳年齢」の有用性は限定的である。「脳年齢」は個人の年齢情報とほぼ重複しており、年齢以外の情報をほとんど提供しない。また、「脳年齢」予測モデルの性能が高いからといって、必ずしも認知機能の説明力が高いわけではない。さらに、MRIデータから直接認知機能を予測するモデルを使うことで、「脳年齢」では捉えられない認知機能の変動を捉えられることが示された。


coremsg

Fluid cognition usually declines as people grow older. For decades, neuroscientists have been on a quest to search for a biomarker that can help capture fluid cognition. One well-known candidate is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI data. Here we aim to formally evaluate the utility of Brain Age as a biomarker for capturing fluid cognition among older individuals. Using 504 aging participants (36-100 years old) from the Human Connectome Project in Aging, we created 26 age-prediction models for Brain Age based on different combinations of MRI modalities. We first tested how much Brain Age from these age-prediction models added to what we had already known from a person’s chronological age in capturing fluid cognition. Based on the commonality analyses, we found a large degree of overlap between Brain Age and chronological age, so much so that, at best, Brain Age could uniquely add only around 1.6% in explaining variation in fluid cognition. Next, the age-prediction models that performed better at predicting chronological age did NOT necessarily create better Brain Age for capturing fluid cognition over and above chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining fluid cognition, thus not improving the models’ utility to capture cognitive abilities. Lastly, we tested how much Brain Age missed the variation in the brain MRI that could explain fluid cognition. To capture this variation in the brain MRI that explained fluid cognition, we computed Brain Cognition, or a predicted value based on prediction models built to directly predict fluid cognition (as opposed to chronological age) from brain MRI data. We found that Brain Cognition captured up to an additional 11% of the total variation in fluid cognition that was missing from the model with only Brain Age and chronological age, leading to around a 1/3-time improvement of the total variation explained. Accordingly, we demonstrated the limited utility of Brain Age as a biomarker for fluid cognition and made some suggestions to ensure the utility of Brain Age in explaining fluid cognition and other phenotypes of interest.

### Competing Interest Statement

The authors have declared no competing interest.

The (Limited?) Utility of Brain Age as a Biomarker for Capturing Fluid Cognition in Older Individuals

高齢者の認知機能低下を捉えるバイオマーカーとしての-脳年齢-の限界的な有用性


高齢者の認知機能低下を捉えるバイオマーカーとしての「脳年齢」の限界的な有用性
