A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia

Abstract

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 and new method 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 betweendataset 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 taskspecific group-discriminative brain networks. In summary, we compared the new approach with 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.

Discriminant analysis of functional connectivity patterns on Grassmann manifold

“…If the component selection method is powerful enough, the prior information might be not necessary. Furthermore, the performance of the proposed method might be improved if it is used in conjunction with functional brain network discovery techniques capable of identifying discriminative brain networks (Sui et al, 2010).…”

Mental stress assessment using simultaneous measurement of EEG and fNIRS

“…We propose joint Independent Component Analysis (jICA) to fuse EEG and fNIRS measurements [53][54][55]. The jICA technique has been previously developed for integrating EEG and fMRI signals, to improve spatio-temporal resolution [56,57]. Whilst the fusion may be achieved by several approaches, we used the fNIRS response to identify key EEG measurement nodes, and then introduced jICA fusion technique to integrate features from both modalities at feature level.…”

Analysis on Image Fusion Techniques for Medical Applications

“…In the medical imaging studies collection of multiple-task brain imaging data from the same subject has now become very common practice [22]. In this paper, Jing Sui , Tulay Adali ,Godfrey Pearlson, Honghui Yang, Scott R. Sponheim ,Tonya White, Vince D. Calhoun put forward a simple yet effective model, "CCA+ICA", as a powerful tool for multitask data fusion.…”