Linked 4-Way Multimodal Brain Differences in Schizophrenia in a Large Chinese Han Population

Abstract

Multimodal fusion has been regarded as a promising tool to discover covarying patterns of multiple imaging types impaired in brain diseases, such as schizophrenia (SZ). In this article, we aim to investigate the covarying abnormalities underlying SZ in a large Chinese Han population (307 SZs, 298 healthy controls [HCs]). Four types of magnetic resonance imaging (MRI) features, including regional homogeneity (ReHo) from resting-state functional MRI, gray matter volume (GM) from structural MRI, fractional anisotropy (FA) from diffusion MRI, and functional network connectivity (FNC) resulted from group independent component analysis, were jointly analyzed by a data-driven multivariate fusion method. Results suggest that a widely distributed network disruption appears in SZ patients, with synchronous changes in both functional and structural regions, especially the basal ganglia network, salience network (SAN), and the frontoparietal network. Such a multimodal coalteration was also replicated in another independent Chinese sample (40 SZs, 66 HCs). Our results on auditory verbal hallucination (AVH) also provide evidence for the hypothesis that prefrontal hypoactivation and temporal hyperactivation in SZ may lead to failure of executive control and inhibition, which is relevant to AVH. In addition, impaired working memory performance was found associated with GM reduction and FA decrease in SZ in prefrontal and superior temporal area, in both discovery and replication datasets. In summary, by leveraging multiple imaging and clinical information into one framework to observe brain in multiple views, we can integrate multiple inferences about SZ from large-scale population and offer unique perspectives regarding the missing links between the brain function and structure that may not be achieved by separate unimodal analyses.

Keywords: MCCA + jICA; auditory hallucination; diffusion MRI; functional network connectivity (FNC); multimodal fusion; resting-state fMRI; schizophrenia; structural MRI.

Fig. 1.

The identified joint group-discriminative components (IC12) in the discovery cohort. (A) Regional homogeneity (ReHo) component; (B) gray matter (GM) component; (C) fractional anisotropy (FA) component; The spatial maps (top) of ReHo, GM, and FA were visualized at |Z| >2.5, with the positive Z scores shown in red. The boxplot and loadings of each component are shown below with schizophrenia (SZ) in red and healthy control (HC) in blue. (D) Functional network connectivity (FNC) component: the FNC matrix (left) was transformed into Z scores and thresholded at |Z| >3 (middle, red nodes), which is displayed through the BrainNet Viewer toolbox (http://www.nitrc.org/projects/bnv/). The red key node size denotes FNC degree.

Fig. 2.

The identified joint group-discriminative components (IC12) in the validation test. (A) Regional homogeneity (ReHo) component; (B) gray matter (GM) component; (C) fractional anisotropy (FA) component; The spatial maps (top) of ReHo, GM, and FA were visualized at |Z| > 2.5, with the positive Z scores shown in red. The boxplot and loadings of each component are shown below with schizophrenia (SZ) in red and healthy control (HC) in blue. (D) Functional network connectivity (FNC) component: the FNC matrix (left) was transformed into Z scores and thresholded at |Z| >3(middle, red, and black nodes), in which red nodes and red lines are overlapped with those key nodes and lines discriminated in the joint independent component (IC) in the discovery cohort.

Fig. 3.

Spatial maps and scatter plot of the 3 components significantly correlated with Auditory Hallucinations Rating Scale (AHRS). (A) Regional homogeneity (ReHo) IC8; (B) gray matter (GM) IC13; the spatial maps (top) of ReHo and GM were visualized at |Z| >2.5, with the positive Z scores shown in red. (C) Functional network connectivity (FNC) IC1: the FNC component was also transformed into Z scores and visualized by BrainNet Viewer, in which red nodes are key nodes thresholded at |Z| >3 (the node size denotes FNC degree), and red lines means healthy controls (HCs) have higher functional connectivity (FC) strength than schizophrenia (SZ), while blue lines means HCs have lower FC strength than SZs. These edges and nodes of FNC_IC1 were mapped upon the surface mapping of ReHo IC8 in (C), indicating a high coherence between ReHo and FNC component associated with AHRS, while all 3 components demonstrate high spatial consistence.

Fig. 4.

The identified components associated with working memory ability from 4-way fusion analysis within each cohort separately. All correlations are significant after controlling group effect as shown above. (A, B): gray matter (GM) and fractional anisotropy (FA) component from discovery cohort. (C, D): GM and FA components from validation cohort. Note that (A) and (C), (B) and (D) demonstrate a relatively high spatial consistence (overlap), with significant 3D spatial correlation r = .64 and r = .16 for GM and FA, respectively, P < 1.0e−10.