Abstract
Association and prediction studies of the brain target the biological consequences of aging and their impact on brain function. Such studies are conducted using different smoothing levels and parcellations at the preprocessing stage, on which their results are dependent. However, the impact of these parameters on the relationship between association values and prediction accuracy is not established. In this study, we used cortical thickness and its relationship with age to investigate how different smoothing and parcellation levels affect the detection of age-related brain correlates as well as brain age prediction accuracy. Our main measures were resel numbers—resolution elements—and age-related variance explained. Using these common measures enabled us to directly compare parcellation and smoothing effects in both association and prediction studies. In our sample of N = 608 participants with age range 18–88, we evaluated age-related cortical thickness changes as well as brain age prediction. We found a negative relationship between prediction performance and correlation values for both parameters. Our results also quantify the relationship between delta age estimates obtained based on different processing parameters. Furthermore, with the direct comparison of the two approaches, we highlight the importance of correct choice of smoothing and parcellation parameters in each task, and how they can affect the results of the analysis in opposite directions.
Highlights
From a biological standpoint, aging is defined by the structural and functional alterations in living organisms (López-Otín et al, 2013)
We examined the effect of smoothing and parcellation on delta age estimation: Y=Xβ1−δ1 δ1=Xβ1−Y
We compared the effect of different smoothing and parcellation on associations between cortical thickness and chronological age as well as brain age prediction accuracy
Summary
From a biological standpoint, aging is defined by the structural and functional alterations in living organisms (López-Otín et al, 2013). The difference between predicted age and chronological age is defined as “delta” or brain age gap estimate i.e., “BrainAGE” to compare the subjects’ chronological age with the Association vs Prediction in Brain Age predicted brain age in a given reference population (Franke et al, 2012; Cole and Franke, 2017; Franke and Gaser, 2019; Smith et al, 2019) Both age related brain alterations and delta age have been studied and used extensively in the neuroimaging literature. Prediction tasks face a trade-off between a more accurate whole brain model with no regional specificity vs a model with lower accuracy and increased spatial resolution (Cole and Franke, 2017; Franke and Gaser, 2019) This limitation results in a more indirect relationship between delta age and other phenotypes without a direct mechanistic and biological model. While evidence supports the application of delta age as a valuable measure to study aging in health and disease, it has been criticized due to its reliance on prediction accuracy (i.e., more accurate models result in lower delta values) (Cole and Franke, 2017)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
