A Joint Analysis of Multi-Paradigm fMRI Data With Its Application to Cognitive Study

Graph Signal Smoothness Based Feature Learning of Brain Functional Networks in Schizophrenia

In this paper we study the brain functional network of schizophrenic patients based on resting-state fMRI data. Different from the region of interest (ROI)-level brain networks that describe the connectivity between brain regions, this paper constructs a subject-level brain functional network that describes the similarity between subjects from a graph signal processing (GSP) perspective. Based on the subject graph, we introduce the concept of graph signal smoothness to analyze the abnormal brain regions (feature brain regions) in which schizophrenic patients produce abnormal functional connections and to quantitatively rank the degree of abnormality of brain regions. We find that in the patients' brain networks, many new connections appear and some common connections are strengthened. The feature brain regions can be easily found according to the value of connection differences. Finally, we validate the learned feature brain regions by the results of two types of statistical analyses (ROI-to-ROI analysis and seed-to-voxel analysis), and the feature brain regions derived from graph signal smoothness are indeed the brain regions with significant differences in the statistical analysis, which illustrates the potential of graph signal smoothness for use in quantitative analysis of brain networks.

A tale of two connectivities: intra- and inter-subject functional connectivity jointly enable better prediction of social abilities

Naturalistic functional magnetic resonance imaging (fMRI) paradigms, such as movie viewing, are attracting increased attention, given their ability to mimic the real-world cognitive demands on attention and multimodal sensory integration. Moreover, naturalistic paradigms allow for characterizing brain network responses associated with dynamic social cognition in a model-free manner using inter-subject functional connectivity (ISFC). While intra-subject functional connectivity (FC) characterizes the individual’s brain functional architecture, ISFC characterizes the neural coupling driven by time-locked extrinsic dynamic stimuli across individuals. Here, we hypothesized that ISFC and FC provide distinct and complementary information about individual differences in social cognition. To test this hypothesis, we examined a public movie-viewing fMRI dataset with 32 healthy adults and 90 typically developing children. Building three partial least squares regression (PLS) models to predict social abilities using FC and/or ISFC, we compared predictive performance to determine whether combining two connectivity measures could improve the prediction accuracy of individuals’ social-cognitive abilities measured by a Theory of Mind (ToM) assessment. Our results indicated that the joint model (ISFC + FC) yielded the highest predictive accuracy and significantly predicted individuals’ social cognitive abilities (rho = 0.34, p < 0.001). We also confirmed that the improved accuracy was not due to the increased feature dimensionality. In conclusion, we demonstrated that intra-/inter-subject connectivity encodes unique information about social abilities, and a joint investigation could help us gain a more complete understanding of the complex processes supporting social cognition.

The Value of Nuclear Magnetic Resonance in Liver Nodular Lesions

In order to analyze the value of contrast-enhanced ultrasound (CEUS) combined with functional magnetic resonance imaging (fMRI) in the early differential diagnosis of liver nodular lesions, the authors studied the value of MRI in liver nodular lesions. A total of 82 patients with liver nodular lesions admitted to the hospital were selected for retrospective analysis; all of them underwent CEUS and fMRI examinations, and taking a biopsy or postoperative pathological examination results as the gold standard, the diagnostic value of CEUS, fMRI single item, and the two combined examinations for liver nodular lesions was analyzed by four-table. The biopsy or postoperative pathological examination results showed that a total of 88 lesions were detected in 82 patients, including 51 patients with benign lesions, with 54 lesions, and 31 patients with malignant lesions, with 34 lesions. Taking biopsy or pathological examination results as the gold standard, the four-table analysis CEUS had a sensitivity of 79.63%, a specificity of 82.35%, an accuracy of 80.68%, and a Kappa value of 0.603 for diagnosing benign and malignant liver nodular lesions. The sensitivity of fMRI in diagnosing benign and malignant liver nodular lesions was 83.33%, the specificity was 85.29%, the accuracy was 84.09%, and the Kappa value was 0.672; the combined sensitivity of the two in the diagnosis of benign and malignant liver nodular lesions was 94.44%, the specificity was 91.18%, the accuracy was 93.18%, and the Kappa value was 0.856, both of which were superior to single detection, and the difference in accuracy was statistically significant (χ²  = 5.683, P < 0.05). CEUS and fMRI have a certain value in the differential diagnosis of liver nodular lesions; the combination of the two can improve the diagnostic sensitivity and accuracy, and has more clinical application value.