Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA. +. ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA. +. ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA. +. ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data.
Bibliographical noteFunding Information:
This work was supported by the National Institutes of Health grants 1 R01 EB 006841 and 1 R01 EB 005846 (to Vince D. Calhoun) and MH43775 , MH074797 and MH077945 (to Godfrey D. Pearlson). We thank the research staff at the University of New Mexico, Minnesota, Iowa and the Mind Research Network who helped collect and process the data. We also appreciate the valuable advice given by Jingyu Liu at the Mind Research Network and Yi-Ou Li at University of California, San Francisco.
- Brain imaging data fusion
- Canonical correlation analysis (CCA)
- Independent component analysis (ICA)
- Joint blind source separation