The relationships between dynamic resting-state networks and social behavior in autism spectrum disorder revealed by fuzzy entropy-based temporal variability analysis of large-scale network.

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by a core deficit in social processes. However, it is still unclear whether the core clinical symptoms of the disorder can be reflected by the temporal variability of resting-state network functional connectivity (FC). In this article, we examined the large-scale network FC temporal variability at the local region, within-network, and between-network levels using the fuzzy entropy technique. Then, we correlated the network FC temporal variability to social-related scores. We found that the social behavior correlated with the FC temporal variability of the precuneus, parietal, occipital, temporal, and precentral. Our results also showed that social behavior was significantly negatively correlated with the temporal variability of FC within the default mode network, between the frontoparietal network and cingulo-opercular task control network, and the dorsal attention network. In contrast, social behavior correlated significantly positively with the temporal variability of FC within the subcortical network. Finally, using temporal variability as a feature, we construct a model to predict the social score of ASD. These findings suggest that the network FC temporal variability has a close relationship with social behavioral inflexibility in ASD and may serve as a potential biomarker for predicting ASD symptom severity.

Altered default mode network causal connectivity patterns in autism spectrum disorder revealed by Liang information flow analysis.

Autism spectrum disorder (ASD) is a pervasive developmental disorder with severe cognitive impairment in social communication and interaction. Previous studies have reported that abnormal functional connectivity patterns within the default mode network (DMN) were associated with social dysfunction in ASD. However, how the altered causal connectivity pattern within the DMN affects the social functioning in ASD remains largely unclear. Here, we introduced the Liang information flow method, widely applied to climate science and quantum mechanics, to uncover the brain causal network patterns in ASD. Compared with the healthy controls (HC), we observed that the interactions among the dorsal medial prefrontal cortex (dMPFC), ventral medial prefrontal cortex (vMPFC), hippocampal formation, and temporo-parietal junction showed more inter-regional causal connectivity differences in ASD. For the topological property analysis, we also found the clustering coefficient of DMN and the In-Out degree of anterior medial prefrontal cortex were significantly decreased in ASD. Furthermore, we found that the causal connectivity from dMPFC to vMPFC was correlated with the clinical symptoms of ASD. These altered causal connectivity patterns indicated that the DMN inter-regions information processing was perturbed in ASD. In particular, we found that the dMPFC acts as a causal source in the DMN in HC, whereas it plays a causal target in ASD. Overall, our findings indicated that the Liang information flow method could serve as an important way to explore the DMN causal connectivity patterns, and it also can provide novel insights into the nueromechanisms underlying DMN dysfunction in ASD.

A Model Combining Multi Branch Spectral-Temporal CNN, Efficient Channel Attention, and LightGBM For MI-BCI Classification.

Accurately decoding motor imagery (MI) brain-computer interface (BCI) tasks has remained a challenge for both neuroscience research and clinical diagnosis. Unfortunately, less subject information and low signal-to-noise ratio of MI electroencephalography (EEG) signals make it difficult to decode the movement intentions of users. In this study, we proposed an end-to-end deep learning model, a multi-branch spectral-temporal convolutional neural network with channel attention and LightGBM model (MBSTCNN-ECA-LightGBM), to decode MI-EEG tasks. We first constructed a multi branch CNN module to learn spectral-temporal domain features. Subsequently, we added an efficient channel attention mechanism module to obtain more discriminative features. Finally, LightGBM was applied to decode the MI multi-classification tasks. The within-subject cross-session training strategy was used to validate classification results. The experimental results showed that the model achieved an average accuracy of 86% on the two-class MI-BCI data and an average accuracy of 74% on the four-class MI-BCI data, which outperformed current state-of-the-art methods. The proposed MBSTCNN-ECA-LightGBM can efficiently decode the spectral and temporal domain information of EEG, improving the performance of MI-based BCIs.

Resting state basal ganglia network in idiopathic generalized epilepsy.

The basal ganglia, a brain structure related to motor control, is implicated in the modulation of epileptic discharges generalization in patients with idiopathic generalized epilepsy (IGE). Using group independent component analysis (ICA) on resting-state fMRI data, this study identified a resting state functional network that predominantly consisted of the basal ganglia in both healthy controls and patients with IGE. In order to gain a better understanding of the basal ganglia network(BGN) in IGE patients, we compared the BGN functional connectivity of controls with that of epilepsy patients, either with interictal epileptic discharges (with-discharge period, WDP) or without epileptic discharge (nondischarge period, NDP) while scanning. Compared with controls, functional connectivity of BGN in IGE patients demonstrated significantly more integration within BGN except cerebellum and supplementary motor area (SMA) during both periods. Compared with the NDP group, the increased functional connectivity was found in bilateral caudate nucleus and the putamen, and decreases were observed in the bilateral cerebellum and SMA in WDP group. In accord with the proposal that the basal ganglia modulates epileptic discharge activity, the results showed that the modulation enhanced the integration in BGN of patients, and modulation during WDP was stronger than that during NDP. Furthermore, reduction of functional connectivity in cerebellum and SMA, the abnormality might be further aggravated during WDP, was consistent with the behavioral manifestations with disturbed motor function in IGE. These resting-state fMRI findings in the current study provided evidence confirming the role of the BGN as an important modulator in IGE.

Diffusion and volumetry abnormalities in subcortical nuclei of patients with absence seizures.

PURPOSE: The thalamus and basal ganglia play an important role in the propagation and modulation of generalized spike and slow-wave discharges (SWDs) in absence epilepsy. Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique sensitive to microstructural abnormalities of cerebral tissue by quantification of diffusion parameter. The purpose of this study is to investigate the diffusion and volume changes in the basal ganglia and thalamus of patients with absence seizures. METHODS: In 11 patients with absence seizures and 11 controls, the thalamus, caudate nucleus, putamen, and pallidum were segmented using an automated atlas-based method on the DTI and three-dimensional (3D) anatomic T1 -weighted images. Then the fractional anisotropy (FA), mean diffusivity (MD), and volume were extracted and quantified. KEY FINDINGS: Compared with controls, patients reveal increased MD values bilaterally in thalamus, putamen, and left caudate nucleus; increased FA value in bilateral caudate nuclei; and loss of volume in bilateral thalamus, putamen, and pallidum. Significant correlations were observed between age of onset and diffusion parameter alterations in caudate nucleus or putamen. SIGNIFICANCE: These findings provide preliminary evidence demonstrating that microstructural changes of subcortical structures are related to the chronic abnormal epileptic activity, and add further evidence for the involvement of thalamus and basal ganglia in propagation and modulation of SWDs in absence epilepsy. These results also indicate that DTI is more sensitive for detection of abnormal structure than the conventional MRI, and it may be adopted as a noninvasive means to understand the pathophysiologic evolution of absence seizures.

Structural and functional changes mapped in the brains of amyotrophic lateral sclerosis patients with/without dysphagia: a pilot study.

The purpose of this study was to explore cerebral structural and functional changes in amyotrophic lateral sclerosis (ALS) patients with or without dysphagia compared with healthy adults. In total, five ALS patients with dysphagia, five ALS patients without dysphagia and 10 healthy controls were evaluated using diffusion tensor magnetic resonance imaging (DTI) and event-related functional magnetic resonance imaging (fMRI) while laryngeal swallow-related movements were recorded. The fMRI data were analysed using the general linear model to gain the differential statistical map (two-sample t-test) for each group. Maps of fractional anisotropy (FA) and mean diffusivity (MD) were calculated within the masks that corresponded to the different statistical functional maps of intergroup comparisons. During the voluntary saliva swallowing, prominent activation of foci corresponded to the primary sensorimotor (SM) cortex in both ALS and controls, while decreased activation of the SM cortex was observed in ALS patients with dysphagia. DTI analysis revealed that FA was significantly reduced and MD was typically increased in the posterior limb of the internal capsule, thalamus, and anterior cingulate gyrus, as well as in the insula of ALS patients compared with controls. However, in ALS patients with dysphagia, FA and MD were more sensitive to these changes than ALS patients without dysphagia. This study highlights the potential of DTI and fMRI for monitoring structural degeneration and functional changes in patients with ALS. This study is the first to demonstrate that cerebral activation map changes correspond to distribution patterns of diffusion abnormalities. Combined non-invasive neuroimaging techniques may be useful tools to assess prognosis and study rehabilitation strategies for dysphagic ALS patients, especially for patients who are MRI-negative by conventional methods.

Diffusion tensor tractography reveals disrupted structural connectivity in childhood absence epilepsy.

PURPOSE: The structural connection patterns of the human brain are the underlying bases for functional connectivity. Although abnormal functional connectivity has been uncovered in childhood absence epilepsy (CAE) in previous electroencephalography and functional magnetic resonance imaging studies, little is known regarding the structural connectivity in CAE. We hypothesized that the structural connectivity would be disrupted in response to the decreased brain function in CAE. METHODS: Diffusion tensor imaging tractography was utilized to map the white matter (WM) structural network, composed of 90 cortical and sub-cortical regions, in 18 CAE and 18 age- and gender-matched healthy controls. Graph theoretical methods were applied to investigate the alterations in the topological and nodal properties of the networks in these patients. RESULTS: Both the CAE and the controls showed small-world properties in their WM networks. However, the network connection strength, absolute clustering coefficient, and global/local efficiency were significantly decreased, but characteristic path length was significantly increased in the CAE compared with the controls. Significantly decreased WM connections, nodal properties, and impaired sub-networks were found in the sub-cortical structures, orbitofrontal area, and limbic cortex in the CAE. Moreover, network connection strength, local efficiency, and nodal features in some regions were significantly negatively correlated with the duration of epilepsy. CONCLUSIONS: The present study demonstrated, for the first time, the disrupted topological organization of WM networks in CAE. The decreased connectivity and efficiency in the orbitofrontal and sub-cortical regions may serve as anatomical evidence to support the functional abnormalities related to the epileptic discharges observed in CAE. Moreover, the orbitofrontal sub-network may play a key role in CAE. These findings open up new avenues toward the understanding of absence epilepsy.