Blunted neural response to emotional faces in the fusiform and superior temporal gyrus may be marker of emotion recognition deficits in pediatric epilepsy

Gray matter volume alterations in de novo Parkinson's disease: A mediational role in the interplay between sleep quality and anxiety

OBJECTIVE

Parkinson's disease (PD) is increasingly recognized for its non-motor symptoms, among which emotional disturbances and sleep disorders frequently co-occur. The commonality of neuroanatomical underpinnings for these symptoms is not fully understood. This study is intended to investigate the differences in gray matter volume (GMV) between PD patients with anxiety (A-PD) and those without anxiety (NA-PD). Additionally, it seeks to uncover the interplay between GMV variations and the manifestations of anxiety and sleep quality.

METHODS

A total of 37 A-PD patients, 43 NA-PD patients, and 36 healthy controls (HCs) were recruited, all of whom underwent voxel-based morphometry (VBM) analysis. Group differences in GMV were assessed using analysis of covariance (ANCOVA). Partial correlation between GMV, anxiety symptom, and sleep quality were analyzed. Mediation analysis explored the mediating role of the volume of GMV-distinct brain regions on the relationship between sleep quality and anxiety within the PD patient cohort.

RESULTS

A-PD patients showed significantly lower GMV in the fusiform gyrus (FG) and right inferior temporal gyrus (ITG) compared to HCs and NA-PD patients. GMV in these regions correlated negatively with Hamilton Anxiety Rating Scale (HAMA) scores (right ITG: r = -0.690, p < 0.001; left FG: r = -0.509, p < 0.001; right FG: r = -0.576, p < 0.001) and positively with sleep quality in PD patients (right ITG: r = 0.592, p < 0.001; left FG: r = 0.356, p = 0.001; right FG: r = 0.470, p < 0.001). Mediation analysis revealed that GMV in the FG and right ITG mediated the relationship between sleep quality and anxiety symptoms, with substantial effect sizes accounted for by the right ITG (25.74%) and FG (left: 11.90%, right: 15.59%).

CONCLUSION

This study has shed further light on the relationship between sleep disturbances and anxiety symptoms in PD patients. Given the pivotal roles of the FG and the ITG in facial recognition and the recognition of emotion-related facial expressions, our findings indicate that compromised sleep quality, under the pathological conditions of PD, may exacerbate the reduction in GMV within these regions, impairing the recognition of emotional facial expressions and thereby intensifying anxiety symptoms.

Progressive structural damage in sleep-related hypermotor epilepsy

This study aimed to explore the alterations in gray matter volume (GMV) based on high-resolution structural data and the temporal precedence of structural alterations in patients with sleep-related hypermotor epilepsy (SHE). After preprocessing of T1 structural images, the voxel-based morphometry and source-based morphometry (SBM) methods were applied in 60 SHE patients and 56 healthy controls to analyze the gray matter volumetric alterations. Furthermore, a causal network of structural covariance (CaSCN) was constructed using Granger causality analysis based on structural data of illness duration ordering to assess the causal impact of structural changes in abnormal gray matter regions. The GMVs of SHE patients were widely reduced, mainly in the bilateral cerebellums, fusiform gyri, the right angular gyrus, the right postcentral gyrus, and the left parahippocampal gyrus. In addition to those regions, the results of the SBM analysis also found decreased GMV in the bilateral frontal lobes, precuneus, and supramarginal gyri. The analysis of CaSCN showed that along with disease progression, the cerebellum was the prominent node that tended to affect other brain regions in SHE patients, while the frontal lobe was the transition node and the supramarginal gyrus was the prominent node that may be easily affected by other brain regions. Our study found widely affected regions of decreased GMVs in SHE patients; these regions underlie the morphological basis of epileptic networks, and there is a temporal precedence relationship between them.

Sex differences in resting state functional connectivity across the first two years of life

Sex differences in behavior have been reported from infancy through adulthood, but little is known about sex effects on functional circuitry in early infancy. Moreover, the relationship between early sex effects on the functional architecture of the brain and later behavioral performance remains to be elucidated. In this study, we used resting-state fMRI and a novel heatmap analysis to examine sex differences in functional connectivity with cross-sectional and longitudinal mixed models in a large cohort of infants (n = 319 neonates, 1-, and 2-year-olds). An adult dataset (n = 92) was also included for comparison. We investigated the relationship between sex differences in functional circuitry and later measures of language (collected in 1- and 2-year-olds) as well as indices of anxiety, executive function, and intelligence (collected in 4-year-olds). Brain areas showing the most significant sex differences were age-specific across infancy, with two temporal regions demonstrating consistent differences. Measures of functional connectivity showing sex differences in infancy were significantly associated with subsequent behavioral scores of language, executive function, and intelligence. Our findings provide insights into the effects of sex on dynamic neurodevelopmental trajectories during infancy and lay an important foundation for understanding the mechanisms underlying sex differences in health and disease.

SD-CNN: A static-dynamic convolutional neural network for functional brain networks

Static functional connections (sFCs) and dynamic functional connections (dFCs) have been widely used in the resting-state functional MRI (rs-fMRI) analysis. sFCs, calculated based on entire rs-fMRI scans, can accurately describe the static topology of the brain network. dFCs, estimated by dividing rs-fMRI scans into a series of short sliding windows, are used to reveal time-varying changes in FC patterns. Currently, how to jointly use sFCs and dFCs to identify brain diseases under the framework of deep learning is still a hot issue. To this end, we propose a static-dynamic convolutional neural network for functional brain networks, which involves a static pathway and a dynamic pathway for taking full advantages of sFCs and dFCs. Specifically, the static pathway, using high-resolution convolution filters (i.e., convolution filters with a high number of channels) at a single adjacency matrix of sFCs, is performed to capture static FC patterns. The dynamic pathway, using low-resolution convolution filters at each adjacency matrix of dFCs, is performed to capture time-varying FC patterns. Two types of diffusion connections are used in this model for encouraging the transfer of information between the static pathway and the dynamic pathway, which can make the learned features more discriminative. Furthermore, a static and dynamic combination classifier is introduced to combine features from two pathways for identifying brain diseases. Experiments on two real datasets demonstrate the effectiveness and advantages of our proposed method.

Functional patterns of neural activation during vocal emotion recognition in youth with and without refractory epilepsy

Epilepsy has been associated with deficits in the social cognitive ability to decode others' nonverbal cues to infer their emotional intent (emotion recognition). Studies have begun to identify potential neural correlates of these deficits, but have focused primarily on one type of nonverbal cue (facial expressions) to the detriment of other crucial social signals that inform the tenor of social interactions (e.g., tone of voice). Less is known about how individuals with epilepsy process these forms of social stimuli, with a particular gap in knowledge about representation of vocal cues in the developing brain. The current study compared vocal emotion recognition skills and functional patterns of neural activation to emotional voices in youth with and without refractory focal epilepsy. We made novel use of inter-subject pattern analysis to determine brain areas in which activation to emotional voices was predictive of epilepsy status. Results indicated that youth with epilepsy were comparatively less able to infer emotional intent in vocal expressions than their typically developing peers. Activation to vocal emotional expressions in regions of the mentalizing and/or default mode network (e.g., right temporo-parietal junction, right hippocampus, right medial prefrontal cortex, among others) differentiated youth with and without epilepsy. These results are consistent with emerging evidence that pediatric epilepsy is associated with altered function in neural networks subserving social cognitive abilities. Our results contribute to ongoing efforts to understand the neural markers of social cognitive deficits in pediatric epilepsy, in order to better tailor and funnel interventions to this group of youth at risk for poor social outcomes.

Abnormal Ventral Somatomotor Network Homogeneity in Patients With Temporal Lobe Epilepsy

BACKGROUND

Abnormalities of functional connectivity in the somatomotor network have been thought to play an essential role in the pathophysiology of epilepsy. However, there has been no network homogeneity (NH) study about the ventral somatomotor network (VSN) in patients with temporal lobe epilepsy (TLE). Therefore, we explored the NH of the VSN in TLE patients in this study.

METHODS

The sample included 52 patients with left temporal lobe epilepsy, 83 patients with right temporal lobe epilepsy, and 68 healthy controls. The NH method was utilized to analyze the resting-state functional magnetic resonance imaging data.

RESULTS

Compared to the controls, rTLE patients had significantly higher NH in the bilateral postcentral gyrus, and significantly lower NH in the bilateral Rolandic operculum and the right superior temporal gyrus (STG). The NH values of the left postcentral gyrus were significantly higher in lTLE patients than in the healthy controls, and lTLE patients had lower NH in the right Rolandic operculum. The altered NH in the postcentral gyrus was negatively correlated with the illness duration, and the decreased NH in the left Rolandic operculum was negatively correlated with the executive control reaction time (ECRT).

CONCLUSION

Our findings suggest that altered NH of the postcentral gyrus, Rolandic operculum and STG might be associated with the pathophysiology of TLE, and thus, highlight the contribution of the VSN to the pathophysiology of TLE.

Amplitude of Low-Frequency Fluctuation With Different Clinical Outcomes in Patients With Generalized Tonic-Clonic Seizures

BACKGROUND

Generalized tonic-clonic seizures (GTCS) are associated with significant disability and sudden unexpected death when they cannot be controlled. We aimed to explore the underlying neural substrate of the different responses to antiseizure drugs between the seizure-free (SF) and non-seizure-free (NSF) patients with GTCS through the amplitude of low-frequency fluctuation (ALFF) method.

METHODS

We calculated ALFF among the SF group, NSF group, and healthy controls (HCs) by collecting resting-state functional magnetic resonance imaging (rs-fMRI) data. One-way ANOVA was used to compare the ALFF of the three groups, and post-hoc analysis was done at the same time. Pearson's correlation analysis between ALFF in the discrepant brain areas and the clinical characteristics (disease course and age of onset of GTCS) was calculated after then.

RESULTS

A significant group effect was found in the right fusiform gyrus (R.FG), left fusiform gyrus (L.FG), left middle occipital gyrus (L.MOG), right inferior frontal gyrus (R.IFG), right precentral gyrus (R.PreG), right postcentral gyrus (R.PostG), and left calcarine sulcus (L.CS). The SF and NSF groups both showed increased ALFF in all discrepant brain areas compared to HCs except the R.IFG in the NSF group. Significantly higher ALFF in the bilateral FG and lower ALFF in the R.IFG were found in the NSF group compared to the SF group.

CONCLUSIONS

Higher ALFF in the bilateral FG were found in the NSF group compared to the SF and HC groups. Our findings indicate that abnormal brain activity in the FG may be one potential neural substrate to interpret the failure of seizure control in patients with GTCS.

Social cognition in children and adolescents with epilepsy: A meta-analysis

Many studies have investigated impairments in two key domains of social cognition (theory of mind [ToM] and facial emotion recognition [FER]) in children and adolescents with epilepsy. However, inconsistent conclusions were found. Our objective was to characterize social cognition performance of children and adolescents with epilepsy. A literature search was conducted using Web of Science, PubMed, and Embase databases. The article retrieval, screening, quality assessment (Newcastle-Ottawa-Scale), and data extraction were performed independently by two investigators. A random-effects model was used to examine estimates. The meta-analysis included 19 studies, with a combined sample of 623 children and adolescents with epilepsy (mean [SD] age, 12.13 [2.62] years; 46.1% female) and 677 healthy controls [HCs]) (mean [SD] age, 11.48 [2.71] years; 50.7% female). The results revealed that relative to HCs, children and adolescents with epilepsy exhibited deficits in ToM (g = -1.08, 95% CI [-1.38, -0.78], p < 0.001, the number of studies [k] = 13), FER (g = -0.98, 95% CI [-1.33, -0.64], p < 0.001, k = 12), and ToM subcomponents (cognitive ToM: g = -1.04, 95% CI [-1.35, -0.72], p < 0.001, k = 12] and affective ToM: g = -0.73, 95% CI [-1.12, -0.34], p < 0.001, k = 8). In addition, there were no statistically significant differences in social cognition deficits between children and adolescents with focal epilepsy and generalized epilepsy. Meta-regressions confirmed the robustness of the results. These quantitative results further deepen our understanding of the two core domains of social cognition in children and adolescents with epilepsy and may assist in the development of cognitive interventions for this patient population. Systematic review registration: https://inplasy.com/inplasy-2022-3-0011/, identifier INPLASY202230011.

Atypical age-related changes in the structure of the mentalizing network in children with refractory focal epilepsy

Refractory focal epilepsy (rFE) is commonly comorbid with impaired social functioning, which significantly reduces quality of life. Previous research has identified a mentalizing network in the brain-composed of the anterior temporal cortex, medial prefrontal cortex (mPFC), posterior temporal sulcus (pSTS), and temporoparietal junction-that is thought to play a critical role in social cognition. In typically-developing (TD) youth, this network undergoes a protracted developmental process with cortical thinning and white matter expansion occurring across adolescence. Because epilepsy is associated with both social dysfunction and irregular neural development, we investigated whether gray and white matter in the mentalizing network differed between youth with rFE (n = 22) and TD youth (n = 41) aged 8-21 years. Older age was associated with reduced cortical thickness in the bilateral mPFC in TD youth, but not in rFE youth. Compared to TD youth, rFE youth had greater white matter density in the right pSTS. Our findings suggest that rFE youth show atypical patterns of cortical thickness and white matter density in regions of the brain that are typically associated with social information processing, potentially as a result of ongoing seizures, comorbid conditions, or other illness-related factors. These results encourage future research to examine whether such variations in neural structure are predictive of specific social deficits in rFE youth.

Social brain networks: Resting-state and task-based connectivity in youth with and without epilepsy

Individuals with epilepsy often experience social difficulties and deficits in social cognition. It remains unknown how disruptions to neural networks underlying such skills may contribute to this clinical phenotype. The current study compared the organization of relevant brain circuits-the "mentalizing network" and a salience-related network centered on the amygdala-in youth with and without epilepsy. Functional connectivity between the nodes of these networks was assessed, both at rest and during engagement in a social cognitive task (facial emotion recognition), using functional magnetic resonance imaging. There were no group differences in resting-state connectivity within either neural network. In contrast, youth with epilepsy showed comparatively lower connectivity between the left posterior superior temporal sulcus and the medial prefrontal cortex-but greater connectivity within the left temporal lobe-when viewing faces in the task. These findings suggest that the organization of a mentalizing network underpinning social cognition may be disrupted in youth with epilepsy, though differences in connectivity within this circuit may shift depending on task demands. Our results highlight the importance of considering functional task-based engagement of neural systems in characterizations of network dysfunction in epilepsy.

Recent Advances in Neuroimaging of Epilepsy

Human neuroimaging has had a major impact on the biological understanding of epilepsy and the relationship between pathophysiology, seizure management, and outcomes. This review highlights notable recent advancements in hardware, sequences, methods, analyses, and applications of human neuroimaging techniques utilized to assess epilepsy. These structural, functional, and metabolic assessments include magnetic resonance imaging (MRI), positron emission tomography (PET), and magnetoencephalography (MEG). Advancements that highlight non-invasive neuroimaging techniques used to study the whole brain are emphasized due to the advantages these provide in clinical and research applications. Thus, topics range across presurgical evaluations, understanding of epilepsy as a network disorder, and the interactions between epilepsy and comorbidities. New techniques and approaches are discussed which are expected to emerge into the mainstream within the next decade and impact our understanding of epilepsies. Further, an increasing breadth of investigations includes the interplay between epilepsy, mental health comorbidities, and aberrant brain networks. In the final section of this review, we focus on neuroimaging studies that assess bidirectional relationships between mental health comorbidities and epilepsy as a model for better understanding of the commonalities between both conditions.