Abstract
Multimodal brain imaging data have shown increasing utility in answering both scientifically interesting and clinically relevant questions. Each brain imaging technique provides a different view of brain function or structure, while multimodal fusion capitalizes on the strength of each and may uncover hidden relationships that can merge findings from separate neuroimaging studies. However, most current approaches have focused on pair-wise fusion and there is still relatively little work on N-way data fusion and examination of the relationships among multiple data types. We recently developed an approach called “mCCA + jICA” as a novel multi-way fusion method which is able to investigate the disease risk factors that are either shared or distinct across multiple modalities as well as the full correspondence across modalities. In this paper, we applied this model to combine resting state fMRI (amplitude of low-frequency fluctuation, ALFF), gray matter (GM) density, and DTI (fractional anisotropy, FA) data, in order to elucidate the abnormalities underlying schizophrenia patients (SZs, n = 35) relative to healthy controls (HCs, n = 28). Both modality-common and modality-unique abnormal regions were identified in SZs, which were then used for successful classification for seven modality-combinations, showing the potential for a broad applicability of the mCCA + jICA model and its results. In addition, a pair of GM-DTI components showed significant correlation with the positive symptom subscale of Positive and Negative Syndrome Scale (PANSS), suggesting that GM density changes in default model network along with white-matter disruption in anterior thalamic radiation are associated with increased positive PANSS. Findings suggest the DTI anisotropy changes in frontal lobe may relate to the corresponding functional/structural changes in prefrontal cortex and superior temporal gyrus that are thought to play a role in the clinical expression of SZ.
Highlights
Multimodal brain imaging techniques are playing increasingly important roles in elucidating structural and functional properties in normal and diseased brains, as well as providing the conceptual glue to bind together data from multiple types or levels of analysis
GM_IC4 were significantly correlated with positive Positive and Negative Syndrome Scale (PANSS) scores, while there was no significant correlation with negative PANSS score
The specific identified regions of the components of interest and their abbreviations are summarized in Table 2 for resting state fMRI components (Talairach labels), Table 3 for DTI (WM tracts), and Table 4 for sMRI (MNI labels) respectively
Summary
Multimodal brain imaging techniques are playing increasingly important roles in elucidating structural and functional properties in normal and diseased brains, as well as providing the conceptual glue to bind together data from multiple types or levels of analysis. Each imaging modality provides a different view of brain function or structure, and data fusion capitalizes on the strengths of each imaging modality/task and their inter-relationships in a joint analysis, creating an important tool to help unravel the black box of psychotic disorders, such as schizophrenia (SZ) (Calhoun et al, 2006; Sui et al, 2012a). Recent advances in data fusion include integrating multiple (task) fMRI data sets (Sui et al, 2009b, 2010; Kim et al, 2010) from the same participant to specify common versus specific sources of activity to a greater degree than traditional general linear model-based approaches This can increase confidence when making conclusions about the functional significance of brain regions and activation changes in brain diseases. A lower and different function–structure connection is often found in patients with SZs compared with healthy controls (HCs)
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