A method for multitask fMRI data fusion applied to schizophrenia.
It is becoming common to collect data from multiple functional magnetic resonance imaging (fMRI) paradigms on a single individual. The data from these experiments are typically analyzed separately and sometimes directly subtracted from one another on a voxel-by-voxel basis. These comparative approaches, although useful, do not directly attempt to examine potential commonalities between tasks and between voxels. To remedy this we propose a method to extract maximally spatially independent maps for each task that are "coupled" together by a shared loading parameter. We first compute an activation map for each task and each individual as "features," which are then used to perform joint independent component analysis (jICA) on the group data. We demonstrate our approach on a data set derived from healthy controls and schizophrenia patients, each of which carried out an auditory oddball task and a Sternberg working memory task. Our analysis approach revealed two interesting findings in the data that were missed with traditional analyses. First, consistent with our hypotheses, schizophrenia patients demonstrate "decreased" connectivity in a joint network including portions of regions implicated in two prevalent models of schizophrenia. A second finding is that for the voxels identified by the jICA analysis, the correlation between the two tasks was significantly higher in patients than in controls. This finding suggests that schizophrenia patients activate "more similarly" for both tasks than do controls. A possible synthesis of both findings is that patients are activating less, but also activating with a less-unique set of regions for these very different tasks. Both of the findings described support the claim that examination of joint activation across multiple tasks can enable new questions to be posed about fMRI data. Our approach can also be applied to data using more than two tasks. It thus provides a way to integrate and probe brain networks using a variety of tasks and may increase our understanding of coordinated brain networks and the impact of pathology upon them.
Calhoun
Vince D
VD
Olin Neuropsychiatry Research Center, Institute of Living, Hartford, Connecticut 06106, USA. vince.calhoun@yale.edu
Adali
Tulay
T
Kiehl
Kent A
KA
Astur
Robert
R
Pekar
James J
JJ
Pearlson
Godfrey D
GD
D000328:Adult; D001921:Brain / Q000473:pathology / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods; D003072:Cognition Disorders / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology; D003627:Data Interpretation, Statistical; D005260:Female; D007839:Functional Laterality / Q000502:physiology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008569:Memory Disorders / Q000175:diagnosis / Q000209:etiology / Q000503:physiopathology; D008570:Memory, Short-Term / Q000502:physiology; D008875:Middle Aged; D008959:Models, Neurological; D009415:Nerve Net / Q000473:pathology / Q000503:physiopathology*; D009434:Neural Pathways / Q000473:pathology / Q000503:physiopathology; D009483:Neuropsychological Tests; D011237:Predictive Value of Tests; D025341:Principal Component Analysis / Q000379:methods; D012559:Schizophrenia / Q000175:diagnosis* / Q000503:physiopathology; D012565:Schizophrenic Psychology