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

Abstract

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 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.

Detection of Mild Traumatic Brain Injury by Machine Learning Classification Using Resting State Functional Network Connectivity and Fractional Anisotropy

“…Further research with a focus in multimodal feature extraction should address differences in feature space and other questions associated with combining fMRI and dMRI. There are several data-driven techniques capable of extracting information from latent variables among which we can mention joint-ICA, 46 parallel ICA. 47 Feature extraction and selection is one important topic that is intrinsically associated with particular latent variables in each modality.…”

Emotional and Motivational Processes in Bipolar Disorder: A Neural Network Perspective

“…In line, reduced white matter integrity in the anterior limb of the internal capsule has also been repeatedly observed in bipolar patients [173,185,186]. Similar, reduced integrity of the uncinate fasciculus, which interconnects the amygdala with the orbitofrontal cortex and the anterior cingulate cortex [187], has also been frequently observed in depressed and euthymic bipolar disorder patients [173,180,182,183,188,189]. Although, increased white matter integrity and increased number of fibers have also been reported for the uncinate fasciculus [180,190].…”

Bayesian inference of structural brain networks

“…A logical extension of our work is to derive connectivity based on the integration of these two imaging modalities. This example of Bayesian data fusion requires that we extend the generative model to take functional data into account as well (Rykhlevskaia et al, 2008;Sui et al, 2011). We can then use structural networks as an informed prior for inference of functional connectivity or infer structural connectivity from both modalities simultaneously.…”