A resting-state fMRI study of temporal lobe epilepsy using multivariate pattern analysis and Granger causality analysis

Abnormal Granger causal connectivity based on altered gray matter volume and associated neurotransmitters of adolescents with internet gaming disorder revealed by a multimodal neuroimaging study

Although prior studies have revealed alterations in gray matter volume (GMV) among individuals with internet gaming disorder (IGD). The brain's multifaceted functions hinge crucially on the intricate connections and communication among distinct regions. However, the intricate interaction of information between brain regions with altered GMV and other regions, and how they synchronize with various neurotransmitter systems, remains enigmatic. Therefore, we aimed to integrate structural, functional and molecular data to explore the GMV-based Granger causal connectivity abnormalities and their correlated neurotransmitter systems in IGD adolescents. Voxel-based morphometry (VBM) analysis was firstly performed to investigate GMV differences between 37 IGD adolescents and 35 matched controls. Brain regions with altered GMV were selected as seeds for further Granger causality analysis (GCA). Two-sample t tests were performed using the SPM12 toolkit to compare the GMV and Granger causal connectivity between IGD and control groups (GRF corrected, Pvoxel<0.005, Pcluster<0.05). Then, GMV-based Granger causal connectivity was spatially correlated with PET- and SPECT-derived maps covering multifarious neurotransmitter systems. Multiple comparison correction was performed using false discovery rate (FDR). Compared with controls, IGD adolescents showed higher GMV in the caudate nucleus and lingual gyrus. For the GCA, IGD adolescents showed higher Granger causal connectivity from insula, putamen, supplementary motor area (SMA) and middle cingulum cortex (MCC) to the caudate nucleus, and lower Granger causal connectivity from superior/inferior parietal gyrus (SPG/IPG) and middle occipital gyrus (MOG) to the lingual gyrus. Besides, GMV-based Granger causal connectivity of IGD adolescents were associated with the dopaminergic, serotonergic, GABAergic and noradrenaline systems. This study revealed that the caudate nucleus and lingual gyrus may be the key sites of neuroanatomical changes in IGD adolescents, and whole-brain Granger causal connectivity abnormalities based on altered GMV involved large brain networks including reward, cognitive control, and visual attention networks, and these abnormalities are associated with a variety of neurotransmitter systems, which may be associated with higher reward sensitivity, cognitive control, and attention control dysfunction.

Hierarchical Bayesian Causality Network to Extract High-Level Semantic Information in Visual Cortex

Functional MRI (fMRI) is a brain signal with high spatial resolution, and visual cognitive processes and semantic information in the brain can be represented and obtained through fMRI. In this paper, we design single-graphic and matched/unmatched double-graphic visual stimulus experiments and collect 12 subjects' fMRI data to explore the brain's visual perception processes. In the double-graphic stimulus experiment, we focus on the high-level semantic information as "matching", and remove tail-to-tail conjunction by designing a model to screen the matching-related voxels. Then, we perform Bayesian causal learning between fMRI voxels based on the transfer entropy, establish a hierarchical Bayesian causal network (HBcausalNet) of the visual cortex, and use the model for visual stimulus image reconstruction. HBcausalNet achieves an average accuracy of 70.57% and 53.70% in single- and double-graphic stimulus image reconstruction tasks, respectively, higher than HcorrNet and HcasaulNet. The results show that the matching-related voxel screening and causality analysis method in this paper can extract the "matching" information in fMRI, obtain a direct causal relationship between matching information and fMRI, and explore the causal inference process in the brain. It suggests that our model can effectively extract high-level semantic information in brain signals and model effective connections and visual perception processes in the visual cortex of the brain.