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

Content Provider	PubMed Central
Author	Sui, Jing Adali, Tülay Godfrey, Pearlson Yang, Honghui Sponheim, Scott White, Tonya Calhoun, Vince D.
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 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 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.
Related Links	http://dx.doi.org/10.1016/j.neuroimage.2010.01.069
Ending Page	134
Page Count	12
Starting Page	123
File Format	PDF
ISSN	10538119
e-ISSN	10959572
Journal	NeuroImage
Issue Number	1
Volume Number	51
Language	English
Publisher Date	2010-05-01
Access Restriction	Open
Subject Keyword	Cognitive Neuroscience Neurology Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Neurology Cognitive Neuroscience

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