Functional magnetic resonance imaging (fMRI) has been implemented widely to study brain connectivity. In the context of fMRI, independent component analysis (ICA) is a powerful tool, which extracts patterns from the data without requiring prior knowledge. Recently, time-varying connectivity analysis has emerged as an important measure to uncover essential knowledge within the network. In this study, we propose a new framework that combines group ICA (GICA) with time varying graphical LASSO (TVGL) to improve the power of analyzing functional network connectivity (FNC) changes. To investigate the performance of our proposed approach, we apply it to capture dynamic FNC using the Pediatric Imaging, Neurocognition, and Genetics (PING) datasets. Our results indicate that females and males of young adults do not show large FNC differences though some slight variations have been found. For instance, females exhibited stronger interdomain FNC and greater correlation in occipital-frontal components for some specific states in comparison to males. In addition, the TVGL-GICA model indicated that females had a higher probability to stay in a stable state. Males had a higher tendency to remain in a globally disconnected mode. Our proposed framework provides a feasible method to investigate brain dynamics accurately and has the potential to become a useful tool in neuroimaging studies.
In recent decades, the graph signal processing techniques have demonstrated their effectiveness in tackling neuroimaging problems. However, most of these tools rely on predefined graphs to conduct spectral analysis, which can not be always satisfied due to the complexity of the brain structure. We, therefore, propose a data-driven signal processing framework (or namely, graph Laplacian learning based Fourier transform) that can effectively estimate the graph structure from the data and conduct Fourier transform afterward to analyze their spectral properties. We validate the proposed method on a large real dataset and the experimental results demonstrate its superiority over traditional methods.
Recently, functional magnetic resonance imaging (fMRI)-derived brain functional connectivity networks (FCNs) have provided insights into explaining individual variation in cognitive and behavioral traits. In these studies, how to accurately construct FCNs is always important and challenging. In this paper, we propose a hypergraph learning based method, which constructs a hypergraph similarity matrix to represent the FCN with hyperedges being generated by sparse regression and their weights being learned by hypergraph learning. The proposed method is capable of better capturing the relations among multiple brain regions than the traditional graph based methods and the existing unweighted hypergraph based method. We then validate the effectiveness of our proposed method on the Philadelphia Neurodevelopmental Cohort data for classifying subjects’ learning ability levels, and discover potential imaging biomarkers which may account for a proportion of the variance in learning ability.
Functional magnetic resonance imaging (fMRI) has been widely used for neuronal connectivity analysis. As a datadriven technique, independent component analysis (ICA) has become a valuable tool for fMRI studies. Recently, due to the dynamic nature of the human brain, time-varying connectivity analysis is regarded as an important measure to reveal essential information within the network. The sliding window approach has been commonly used to extract dynamic information from fMRI time series. However, it has some limitations due to the assumption that connectivity at a given time can be estimated from all the samples of the input time series data spanned by the selected window. To address this issue, we apply a time-varying graphical lasso model (TVGL) proposed by Hallac et al., which can infer the network even when the observation interval is at only one time point. On the other hand, recent results have shown that the individual’s connectivity profiles can be used as “fingerprint” to identify subjects from a large group. We hypothesize that the subject-specific FC profiles may have the critical effect on analyzing FC dynamics at a group level. In this work, we apply a group ICA (GICA) based data-driven framework to assess dynamic functional network connectivity (dFNC), based on the combination of GICA and TVGL. Also, we use the regression model to remove the subject-specific individuality in detecting functional dynamics. The results prove our hypothesis and suggest that removing the individual effect may benefit us to assess the connectivity dynamics within the human brain.