Asymmetric generalizability of multimodal brain‐behavior associations across age‐groups

Abstract

Machine learning methods have increasingly been used to map out brain-behavior associations (BBA), and to predict out-of-scanner behavior of unseen subjects. Given the brain changes that occur in the context of aging, the accuracy of these predictions is likely to depend on how similar the training and testing data sets are in terms of age. To this end, we examined how well BBAs derived from an age-group generalize to other age-groups. We partitioned the CAM-CAN data set (N = 550) into the young, middle, and old age-groups, then used the young and old age-groups to construct prediction models for 11 behavioral outcomes using multimodal neuroimaging features (i.e., structural and resting-state functional connectivity, and gray matter volume/cortical thickness). These models were then applied to all three age-groups to predict their behavioral scores. When the young-derived models were used, a graded pattern of age-generalization was generally observed across most behavioral outcomes-predictions are the most accurate in the young subjects in the testing data set, followed by the middle and then old-aged subjects. Conversely, when the old-derived models were used, the disparity in the predictive accuracy across agegroups was mostly negligible. These findings hold across different imaging modalities.These results suggest the asymmetric age-generalization of BBAs-old-derived BBAs generalized well to all age-groups, however young-derived BBAs generalized poorly beyond their own age-group.

Tell me why? A scoping review on the fundamental building blocks of functional MRI networks

“…Furthermore, 14 papers (7.3%) used alternative approaches to estimate the connections between nodes, such as regression (e.g., Freedberg et al, 2022;Thome et al, 2022;Yu & Fischer, 2022) or coherence measures (e.g., Li et al, 2022;Van Balkom et al, 2022;Van Dijk et al, 2010). A subset of seven papers (3.7%) used multiple approaches to estimate the associations between nodes.…”