Background: Better life satisfaction (LS) is associated with better psychological and psychiatric outcomes. To the best of our knowledge, no studies have examined prediction models for LS.Methods: Using resting-state functional magnetic resonance imaging (R-fMRI) data from the Human Connectome Project (HCP) Young Adult S1200 dataset, we examined whether LS is predictable from intrinsic functional connectivity (iFC). All the HCP data were subdivided into either discovery (n = 100) or validation (n = 766) datasets. Using R-fMRI data in the discovery dataset, we computed a matrix of iFCs between brain regions. Ridge regression, in combination with principal component analysis and 10fold cross-validation, was used to predict LS. Prediction performance was evaluated by comparing actual and predicted LS scores. The generalizability of the prediction model obtained from the discovery dataset was evaluated by applying this model to the validation dataset. Results:The model was able to successfully predict LS in the discovery dataset (r = 0.381, p < .001). The model was also able to successfully predict the degree of LS (r = 0.137, 5000-repetition permutation test p = .006) in the validation dataset, suggesting that our model is generalizable to the prediction of LS in young adults. iFCs stemming from visual, ventral attention, or limbic networks to other networks (such as the dorsal attention network and default mode network) were likely to contribute positively toward predicted LS scores. iFCs within ventral attention and limbic networks also positively contributed to predicting LS. On the other hand, iFCs stemming from the visual and cerebellar networks to other networks were likely to contribute negatively to the predicted LS scores. Conclusion:The present findings suggest that LS is predictable from the iFCs. These results are an important step toward identifying the neural basis of life satisfaction.

No abstract

The achievement of improved subjective well-being (SWB) is a worldwide issue. Prior studies using self-report questionnaires have demonstrated that better social relationships (SR) form the foundation for better SWB. To confirm the relationships with objective measure and investigate the biological underpinning, we conducted connectome-based prediction modeling with a 10-fold cross-validation, utilizing resting state functional magnetic resonance imaging data (n = 761). Two aspects of SWB (life satisfaction and positive affect) were successfully predicted using SR-related functional connections (FCs). The models also showed good prediction performance in a validation sample (n = 91), suggesting that our models are generalizable. All six aspects of SR considered were related to different sets of FCs with minimal overlap in edge level. The contributions of these FC sets to the two aspects of SWB were substantially different. In the future, effort should be made to improve all aspects of SR to achieve better SWB.