Benefits of multi-modal fusion analysis on a large-scale dataset: Life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure

摘要

Neuroimaging studies have become increasingly multimodal in recent years, with researchers typically acquiring several different types of MRI data and processing them along separate pipelines that provide a set of complementary windows into each subject's brain. However, few attempts have been made to integrate the various modalities in the same analysis. Linked ICA is a robust data fusion model that takes multi-modal data and characterizes inter-subject variability in terms of a set of multi-modal components. This paper examines the types of components found when running Linked ICA on a large magnetic resonance imaging (MRI) morphometric and diffusion tensor imaging (DTI) data set comprising 484 healthy subjects ranging from 8 to 85. years of age. We find several strong global features related to age, sex, and intracranial volume; in particular, one component predicts age to a high accuracy (r = 0.95). Most of the remaining components describe spatially localized modes of variability in white or gray matter, with many components including both tissue types. The multimodal components tend to be located in anatomically-related brain areas, suggesting a morphological and possibly functional relationship. The local components show relationships between surface-based cortical thickness and arealization, voxel-based morphometry (VBM), and between three different DTI measures. Further, we report components related to artifacts (e.g. scanner software upgrades) which would be expected in a dataset of this size. Most of the 100 extracted components showed interpretable spatial patterns and were found to be reliable using split-half validation. This work provides novel information about normal inter-subject variability in brain structure, and demonstrates the potential of Linked ICA as a feature-extracting data fusion approach across modalities. This exploratory approach automatically generates models to explain structure in the data, and may prove especially powerful for large-scale studies, where the population variability can be explored in increased detail.