Schizophrenia Shows Disrupted Links between Brain Volume and Dynamic Functional Connectivity

Abstract

Studies featuring multimodal neuroimaging data fusion for understanding brain function and structure, or disease characterization, leverage the partial information available in each of the modalities to reveal data variations not exhibited through the independent analyses. Similar to other complex syndromes, the characteristic brain abnormalities in schizophrenia may be better understood with the help of the additional information conveyed by leveraging an advanced modeling method involving multiple modalities. In this study, we propose a novel framework to fuse feature spaces corresponding to functional magnetic resonance imaging (functional) and gray matter (structural) data from 151 schizophrenia patients and 163 healthy controls. In particular, the features for the functional and structural modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) maps and the intensities of the gray matter (GM) maps, respectively. The dFNC maps are estimated from group independent component analysis (ICA) network time-courses by first computing windowed functional correlations using a sliding window approach, and then estimating subject specific states from this windowed data using temporal ICA followed by spatio-temporal regression. For each subject, the functional data features are horizontally concatenated with the corresponding GM features to form a combined feature space that is subsequently decomposed through a symmetric multimodal fusion approach involving a combination of multiset canonical correlation analysis (mCCA) and joint ICA (jICA). Our novel combined analyses successfully linked changes in the two modalities and revealed significantly disrupted links between GM volumes and time-varying functional connectivity in schizophrenia. Consistent with prior research, we found significant group differences in GM comprising regions in the superior parietal lobule, precuneus, postcentral gyrus, medial/superior frontal gyrus, superior/middle temporal gyrus, insula and fusiform gyrus, and several significant aberrations in the inter-regional functional connectivity strength as well. Importantly, structural and dFNC measures have independently shown changes associated with schizophrenia, and in this work we begin the process of evaluating the links between the two, which could shed light on the illness beyond what we can learn from a single imaging modality. In future work, we plan to evaluate replication of the inferred structure-function relationships in independent partitions of larger multi-modal schizophrenia datasets.

Keywords: dynamic functional connectivity; gray matter; joint ICA; mCCA; multimodal fusion; schizophrenia; structure-function relationship.

Figure 1

Estimation of the functional (fMRI) data feature space. Aggregate states were estimated by decomposing the windowed correlations by temporal ICA. Subject-specific states were next estimated through a spatio-temporal (dual) regression procedure wherein, for each subject, the aggregate states were regressed into the subject's windowed FNC data to estimate subject-specific component time-courses in the first regression step, and the estimated time-courses were regressed into the subject's windowed FNC to derive the subject-specific states in the second regression step; (B) Summary of the mCCA + jICA framework. For each subject, the functional data feature space as estimated in (A) was concatenated with the smoothed, modulated and warped gray matter maps (as the structural data feature space) and fused using the joint “mCCA+jICA” framework. This framework combines the mCCA and jICA algorithms to decompose the observed data into a linear combination of sources mixed through “effective” modality-specific mixing matrices as illustrated above.

Figure 2

Resting State Networks (RSNs). Spatial maps of the 47 retained RSNs at the most activated sagittal, coronal and axial slices.

Figure 3

Joint Source 1. (A) Spatial maps of the most activated regions for the structural component in the first joint source; (B) A visualization of significant links (functional connections with highest connectivity strengths i.e., with z-scores of connectivity strengths: |z| > 3) and their connectivity strengths for the functional component in the first joint source; (C) Scatterplot of the functional data loadings with the structural data loadings revealed a significant correlation (r = −0.28, p = 1.08 × 10⁻⁶); and (D) The group mean for the loading parameters was significantly lower for participants with schizophrenia, thus suggesting significant reductions in gray matter volume for this structural component.

Figure 4

Joint Source 2. (A) Spatial maps of the most activated regions for the structural component in the second joint source; (B) A visualization of significant links (functional connections with highest connectivity strengths i.e., with z-scores of connectivity strengths: |z| > 3) and their connectivity strengths for the functional component in the second joint source; (C) Scatterplot of the functional data loadings with the structural data loadings revealed a significant correlation (r = −0.40, p = 3.91 × 10⁻¹³); and (D) The group mean for the loading parameters was significantly lower for participants with schizophrenia, thus suggesting significant reductions in gray matter volume for this structural component.