Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model

• Published in: NeuroImage (Orlando, Fla.) Vol. 57; no. 3; pp. 839 - 855
• Main Authors: Sui, Jing, Pearlson, Godfrey, Caprihan, Arvind, Adali, Tülay, Kiehl, Kent A., Liu, Jingyu, Yamamoto, Jeremy, Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.08.2011; Elsevier Limited
• Subjects: Adult; Anisotropy; Bipolar disorder; Bipolar Disorder - diagnosis; Brain - physiopathology; Brain Mapping; Confidence intervals; Diagnosis, Differential; Diffusion tensor imaging (DTI); Diffusion Tensor Imaging - methods; Female; Fractional anisotropy (FA); Functional MRI (fMRI); Humans; Image Interpretation, Computer-Assisted - methods; Independent component analysis (ICA); Magnetic Resonance Imaging - methods; Male; Medical imaging; Multimodal canonical correlation analysis (mCCA); Schizophrenia; Schizophrenia - diagnosis; Multimodal canonical correlation analysis (mCCA); Schizophrenia; Fractional anisotropy (FA); Bipolar disorder; Functional MRI (fMRI); Independent component analysis (ICA); Diffusion tensor imaging (DTI)
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2011.05.055

Diverse structural and functional brain alterations have been identified in both schizophrenia and bipolar disorder, but with variable replicability, significant overlap and often in limited number of subjects. In this paper, we aimed to clarify differences between bipolar disorder and schizophrenia by combining fMRI (collected during an auditory oddball task) and diffusion tensor imaging (DTI) data. We proposed a fusion method, “multimodal CCA+ joint ICA”, which increases flexibility in statistical assumptions beyond existing approaches and can achieve higher estimation accuracy. The data collected from 164 participants (62 healthy controls, 54 schizophrenia and 48 bipolar) were extracted into “features” (contrast maps for fMRI and fractional anisotropy (FA) for DTI) and analyzed in multiple facets to investigate the group differences for each pair-wised groups and each modality. Specifically, both patient groups shared significant dysfunction in dorsolateral prefrontal cortex and thalamus, as well as reduced white matter (WM) integrity in anterior thalamic radiation and uncinate fasciculus. Schizophrenia and bipolar subjects were separated by functional differences in medial frontal and visual cortex, as well as WM tracts associated with occipital and frontal lobes. Both patients and controls showed similar spatial distributions in motor and parietal regions, but exhibited significant variations in temporal lobe. Furthermore, there were different group trends for age effects on loading parameters in motor cortex and multiple WM regions, suggesting that brain dysfunction and WM disruptions occurred in identified regions for both disorders. Most importantly, we can visualize an underlying function–structure network by evaluating the joint components with strong links between DTI and fMRI. Our findings suggest that although the two patient groups showed several distinct brain patterns from each other and healthy controls, they also shared common abnormalities in prefrontal thalamic WM integrity and in frontal brain mechanisms. ► fMRI–DTI data fusion across 3 groups (HC, SZ, BP) for 164 subjects. ► Propose a novel general, flexible fusion model based on multimodal CCA+ joint ICA. ► Identify group-discriminating aspects in multiple facets. ► Visualize the potential function–structure network by evaluating the joint ICs.