The Contribution of Brain Structural and Functional Variance in Predicting Age, Sex and Treatment

Structural and functional neuroimaging have been widely used to track and predict demographic and clinical variables, including treatment outcomes. However, it is often difficult to directly establish and compare the respective weights and contributions of brain structure and function in prediction studies. The present study aimed to directly investigate respective roles of brain structural and functional indices, along with their contributions in the prediction of demographic variables (age/sex) and clinical changes of schizophrenia patients. The present study enrolled 492 healthy people from Southwest University Adult Lifespan Dataset (SALD) for demographic variables analysis and 42 patients with schizophrenia from West China Hospital for treatment analysis. We conducted a model fit test with two variables (one voxel-based structural metric and another voxel-based functional metric) and then performed a variance partitioning on the voxels that can be predicted sufficiently. Permutation tests were applied to compare the contribution difference between each pair of structural and functional measurements. We found that voxel-based structural indices had stronger predictive value for age and sex, while voxel-based functional metrics showed stronger predictive value for treatment. Therefore, through variance partitioning, we could clearly and directly explore and compare the voxel-based structural and functional indices on particular variables. In sum, for long-term change variable (age) and constant biological feature (sex), the voxel-based structural metrics would contribute more than voxel-based functional metrics; but for short-term change variable (schizophrenia treatment), the functional metrics could contribute more.