|
pubmed:abstractText |
Single task analysis methods of functional MRI brain data, though useful, are not able to evaluate the joint information between tasks. Data fusion of multiple tasks that probe different cognitive processes provides knowledge of the joint information and may be important in order to better understand complex disorders such as schizophrenia. In this article, we introduce a simple but effective technique to fuse two tasks by computing the histogram of correlations for all possible combinations of whole brain voxels. The approach was applied to data derived from healthy controls and patients with schizophrenia from four different tasks, auditory oddball (target), auditory oddball (novel), Sternberg working memory, and sensorimotor. It was found that in four out of six task combinations patients' intertask correlations were more positively correlated than controls', in one combination the controls showed more positive correlations and in another there was no significant difference. The robustness of this result was checked with several testing techniques. The four task combinations for which patients had more positive correlation occurred at different scanning sessions and the task combination that showed the opposite result occurred within the same scanning session. Brain regions that showed high intertask correlations were found for both groups and regions that correlated differently between the two groups were identified. The approach introduced finds interesting results and new differential features that cannot be achieved through traditional methods.
|
