Schizophrenia (SZ) is a chronic and severe mental disorder that affects how a person thinks, feels, and behaves. It has been widely acknowledged that SZ is related to disrupted brain connectivity; however, the underlying neuromechanism has not been fully understood. In the current literature, various methods have been proposed to estimate the association networks of the brain using functional Magnetic Resonance Imaging (fMRI). Approaches that characterize statistical associations are likely a good starting point for estimating brain network interactions. With in-depth research, it is natural to shift to causal interactions. Therefore, we use the fMRI image from the Mind Clinical Imaging Consortium (MCIC) to study the causal brain network of SZ patients. Existing methods have focused on estimating a single directed graphical model but ignored the similarities from related classes. We, thus, design a two-step Bayesian network analysis for this case-control study, which we assume their brain networks are distinct but related. We reveal that compared to healthy people, SZ patients have a diminished ability to combine specialized information from distributed brain regions. Particularly, we have identified 6 hub brain regions in the aberrant connectivity network, which are at the frontal-parietal lobe (Supplementary motor area, Middle frontal gyrus, Inferior parietal gyrus), insula and putamen of the left hemisphere.
Estimating causal brain networks from fMRI data is important in understanding functional human brain connectivity, and current causality estimation methods face various challenges such as high dimensionality and expensive computation. The joint estimation of causal networks between groups shows promising potential to investigate group-related brain connectivity variations. In this paper, we proposed a joint causal brain network estimation method by adding a prior to the popular PC algorithm1 (by Peter Spirtes and Clark Glymour). The prior is obtained through a fast joint Bayesian analysis (FIBA) and plays a role as a screening step, significantly reducing computational burden of PC algorithm. Moreover, the FIBA also enables us to efficiently address the high dimensionality problem of fMRI data. The experimental results from both simulation data sets and real fMRI data demonstrate the accuracy and efficiency of the proposed method. The specific brain connections identified in schizophrenia patients extend previous research and shed light on other studies of mental disorders.
KEYWORDS: Brain, Simulation of CCA and DLA aggregates, Neuroimaging, Canonical correlation analysis, Genetics, Functional magnetic resonance imaging, Brain imaging, Visualization, Alzheimer's disease, Neurons
Distance correlation is a measure that can detect both linear and nonlinear associations. However, applying distance correlation to imaging genetic studies often needs multiple testing correction due to the large number of multiple inferences. As a result, the sensitivity of its detection may be low. We propose a new model, distance canonical correlation analysis (DCCA), which overcomes this problem by searching a combination of features with the highest distance correlation. This is achieved by constructing a distance kernel function followed by solving a subsequent optimization problem. The ability to detect both linear and nonlinear associations makes DCCA suitable for analyzing complex multimodal and imaging-genetic associations. When applied to a brain imaging-genetic study from the Philadelphia Neurodevelopmental Cohort (PNC), DCCA detected several mental disorder-related gene pathways and brain networks. Experiments on brain connectivity found that the default mode network had strong nonlinear connections with other brain networks. When applied to the study of age effects, DCCA revealed that the connections of brain networks were relatively weak in younger groups but became stronger at older age stages. It indicates that adolescence is a vital stage for brain development. DCCA thus reveals a number of interesting findings and demonstrates a powerful new approach for analyzing multimodal brain imaging data.
Schizophrenia (SZ) is a chronic and severe mental disorder that affects how a person thinks, feels, and behaves. It has been proposed that this disorder is related to disrupted brain connectivity, which has been verified by many studies, but its underlying mechanism is still unclear. Recent advances have combined heterogeneous data including both medical images (e.g., fMRI) and genomic data (e.g., SNPs and DNA methylations), which give rise to a new perspective on SZ. In this paper, we aim to explore the associations between DNA methylations and various brain regions to shed light on the neuro-epigenetic interactions in the SZ disease. We proposed a joint Gaussian copula model, where we used the Gaussian copula model to address the data integration issue and the joint network estimation for different conditions (case-control study). Unlike previous studies using methods such as CCA or ICA, the proposed method not only can provide the neuro-epigenetic interactions but also the brain connectivity, and methylation selfinteractions all at the same time. The data we used were collected by the Mind Clinical Imaging Consortium (MCIC), which includes the fMRI image and the epigenetic information such as methylation levels. The data were from 183 subjects, among them 79 SZ patients and 104 healthy controls. We have identified several hub brain regions and hub DNA methylations of the SZ patients and have also detected 10 methylation-brain ROI interactions for SZ. Our analysis results are shown to be both statistically and biologically significant.
Adolescence is a transitional period between childhood and adulthood with physical changes, as well as increasing emotional activity. Studies have shown that the emotional sensitivity is related to a second dramatical brain growth. However, there is little focus on the trend of brain development during this period. In this paper, we aim to track the functional brain connectivity development in adolescence using resting state fMRI (rs-fMRI), which amounts to a time-series analysis problem. Most existing methods either require the time point to be fairly long or are only applicable to small graphs. To this end, we adapted a fast Bayesian integrative analysis (FBIA) to address the short time-series difficulty, and combined with adaptive sum of powered score (aSPU) test for group difference. The data we used are the resting state fMRI (rs-fMRI) obtained from the publicly available Philadelphia Neurodevelopmental Cohort (PNC). They include 861 individuals aged 8–22 years who were divided into five different adolescent stages. We summarized the networks with global measurements: segregation and integration, and provided full brain functional connectivity pattern in various stages of adolescence. Moreover, our research revealed several brain functional modules development trends. Our results are shown to be both statistically and biologically significant.