| 其他摘要 | Multimodal neuroimaging methods have been widely used in brain research. But for specific brain-related questions, there still lack empirical research that can guide the selection of specific multimodal neuroimaging methods. The fundamental research of brain imaging is mainly related to demographic, and clinical variables. In accordance with the different time scales, this study selected sex (evolutional scale), age (super long-term), diagnose results (long-term), and treatment effect (medium- or short-term) as research variables, and compared the contribution of multimodal indexes to explain the corresponding variables. For specific research questions, we used variance partitioning to compare the contribution of different modal indexes. This method could separate the portion of variances of different multimodal metrics to explain a specific variable. In this study, for specific predictor variables, we conducted a model fit test with two variables (one structural metric and another functional metric) and then performed a variance partitioning that could be explained sufficiently. Permutation tests were applied to compare the contribution difference between each pair of measurements. In study one, we found that voxel-based structural metrics are better than functional metrics in explaining sex, while surface-based analysis did not reveal any findings. As sex differences mainly manifested on the subcortical brain structure, the voxel-based structural metrics might be a better option. In study two, we used similar methods and found that by using voxel-based analysis, structural metrics had better explainable effect than functional metrics for age in most brain areas, while by surface-based analysis, structural metrics and functional metrics had different explaining effects in different brain regions. This indicated that for explaining aging, we should consider to pick structural or functional metrics on certain brain regions of interest. For study three, we found that structural metrics had better explainable effect than functional metrics for discriminating major depression disorder or health control. But in the fourth study, no matter explaining medium-term or short-term treatment, voxel-based functional metrics showed stronger explainable value for treatment. This indicated that relatively short-term alterations like treatment effects may be better revealed by functional metrics. In sum, for constant biological feature (sex), the voxel-based structural metrics may be a better option than voxel-based functional metrics in subcortical areas; for long-term change variable (age), we should carefully pick structural of functional metrics on specific brain areas; for checking whether or not suffering from mental illness (major depression disorder), the voxel-based structural metrics may be a better option too; but for short-term or medium-term change variable (schizophrenia treatment), we should focus more on the functional measurements. |
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