Cortico-striato-thalamo-cerebellar networks of structural covariance underlying different epilepsy syndromes associated with generalized tonic-clonic seizures

Striatum- and Cerebellum-Modulated Epileptic Networks Varying Across States with and without Interictal Epileptic Discharges

Idiopathic generalized epilepsy (IGE) is characterized by cryptogenic etiology and the striatum and cerebellum are recognized as modulators of epileptic network. We collected simultaneous electroencephalogram and functional magnetic resonance imaging data from 145 patients with IGE, 34 of whom recorded interictal epileptic discharges (IEDs) during scanning. In states without IEDs, hierarchical connectivity was performed to search core cortical regions which might be potentially modulated by striatum and cerebellum. Node-node and edge-edge moderation models were constructed to depict direct and indirect moderation effects in states with and without IEDs. Patients showed increased hierarchical connectivity with sensorimotor cortices (SMC) and decreased connectivity with regions in the default mode network (DMN). In the state without IEDs, striatum, cerebellum, and thalamus were linked to weaken the interactions of regions in the salience network (SN) with DMN and SMC. In periods with IEDs, overall increased moderation effects on the interaction between regions in SN and DMN, and between regions in DMN and SMC were observed. The thalamus and striatum were implicated in weakening interactions between regions in SN and SMC. The striatum and cerebellum moderated the cortical interaction among DMN, SN, and SMC in alliance with the thalamus, contributing to the dysfunction in states with and without IEDs in IGE. The current work revealed state-specific modulation effects of striatum and cerebellum on thalamocortical circuits and uncovered the potential core cortical targets which might contribute to develop new clinical neuromodulation techniques.

Mapping progressive damage epicenters in epilepsy with generalized tonic-clonic seizures by causal structural covariance network density (CaSCNd)

AIMS

Mapping progressive patterns of structural damage in epilepsies with idiopathic and secondarily generalized tonic-clonic seizures with causal structural covariance networks and multiple analysis strategies.

METHODS

Patients with idiopathic generalized tonic-clonic seizures (IGTCS) (n = 114) and secondarily generalized tonic-clonic seizures (SGTCS) (n = 125) were recruited. Morphometric parameter of gray matter volume was analyzed on structural MRI. Structural covariance network based on granger causality analysis (CaSCN) was performed on the cross-sectional morphometric data sorted by disease durations of patients. Seed-based CaSCN analysis was firstly carried out to map the progressive and influential patterns of damage to thalamus-related structures. A novel technique for voxel-based CaSCN density (CaSCNd) analysis was further proposed, enabling for identifying the epicenter of structural brain damage during the disease process.

RESULTS

The thalamus-associated CaSCNs demonstrated different patterns of progressive damage in two types of generalized tonic-clonic seizures. In IGTCS, the structural damage was predominantly driven from the thalamus, and expanded to the cortex, while in SGTCS, the damage was predominantly driven from the cortex, and expanded to the thalamus through the basal ganglia. CaSCNd analysis revealed that the IGTCS had an out-effect epicenter in the thalamus, whereas the SGTCS had equipotent in- and out-effects in the thalamus, cortex, and basal ganglia.

CONCLUSION

CaSCN revealed distinct damage patterns in the two types of GTCS, featuring with measurement of structural brain damage from the accumulating effect over a relatively long time period. Our work provided evidence for understanding network impairment mechanism underlying different GTCSs.

Deep learning distinguishes connectomes from focal epilepsy patients and controls: feasibility and clinical implications

The application of deep learning models to evaluate connectome data is gaining interest in epilepsy research. Deep learning may be a useful initial tool to partition connectome data into network subsets for further analysis. Few prior works have used deep learning to examine structural connectomes from patients with focal epilepsy. We evaluated whether a deep learning model applied to whole-brain connectomes could classify 28 participants with focal epilepsy from 20 controls and identify nodal importance for each group. Participants with epilepsy were further grouped based on whether they had focal seizures that evolved into bilateral tonic-clonic seizures (17 with, 11 without). The trained neural network classified patients from controls with an accuracy of 72.92%, while the seizure subtype groups achieved a classification accuracy of 67.86%. In the patient subgroups, the nodes and edges deemed important for accurate classification were also clinically relevant, indicating the model's interpretability. The current work expands the evidence for the potential of deep learning to extract relevant markers from clinical datasets. Our findings offer a rationale for further research interrogating structural connectomes to obtain features that can be biomarkers and aid the diagnosis of seizure subtypes.

Altered Cerebro-Cerebellar Effective Connectivity in New-Onset Juvenile Myoclonic Epilepsy

(1) Objective: Resting-state fMRI studies have indicated that juvenile myoclonic epilepsy (JME) could cause widespread functional connectivity disruptions between the cerebrum and cerebellum. However, the directed influences or effective connectivities (ECs) between these brain regions are poorly understood. In the current study, we aimed to evaluate the ECs between the cerebrum and cerebellum in patients with new-onset JME. (2) Methods: Thirty-four new-onset JME patients and thirty-four age-, sex-, and education-matched healthy controls (HCs) were included in this study. We compared the degree centrality (DC) between the two groups to identify intergroup differences in whole-brain functional connectivity. Then, we used a Granger causality analysis (GCA) to explore JME-caused changes in EC between cerebrum regions and cerebellum regions. Furthermore, we applied a correlation analysis to identify associations between aberrant EC and disease severity in patients with JME. (3) Results: Compared to HCs, patients with JME showed significantly increased DC in the left cerebellum posterior lobe (CePL.L), the right inferior temporal gyrus (ITG.R) and the right superior frontal gyrus (SFG.R), and decreased DC in the left inferior frontal gyrus (IFG.L) and the left superior temporal gyrus (STG.L). The patients also showed unidirectionally increased ECs from cerebellum regions to the cerebrum regions, including from the CePL.L to the right precuneus (PreCU.R), from the left cerebellum anterior lobe (CeAL.L) to the ITG.R, from the right cerebellum posterior lobe (CePL.R) to the IFG.L, and from the left inferior semi-lunar lobule of the cerebellum (CeISL.L) to the SFG.R. Additionally, the EC from the CeISL.L to the SFG.R was negatively correlated with the disease severity. (4) Conclusions: JME patients showed unidirectional EC disruptions from the cerebellum to the cerebrum, and the negative correlation between EC and disease severity provides a new perspective for understanding the cerebro-cerebellar neural circuit mechanisms in JME.

White matter alterations in focal to bilateral tonic-clonic seizures

We examined the white matter of patients with and without focal to bilateral tonic-clonic seizures (FBTCS), and control participants. A neural network based tract segmentation model (Tractseg) was used to isolate tract-specific, track-weighted tensor-based measurements from the tracts of interest. We compared the group differences in the track-weighted tensor-based measurements derived from whole and hemispheric tracts. We identified several regions that displayed significantly altered white matter in patients with focal epilepsy compared to controls. Furthermore, patients without FBTCS showed significantly increased white matter disruption in the inferior fronto-occipital fascicle and the striato-occipital tract. In contrast, the track-weighted tensor-based measurements from the FBTCS cohort exhibited a stronger resemblance to the healthy controls (compared to the non-FBTCS group). Our findings revealed marked alterations in a range of subcortical tracts considered critical in the genesis of seizures in focal epilepsy. Our novel application of tract-specific, track-weighted tensor-based measurements to a new clinical dataset aided the elucidation of specific tracts that may act as a predictive biomarker to distinguish patients likely to develop FBTCS.

Reconfiguration of static and dynamic thalamo-cortical network functional connectivity of epileptic children with generalized tonic-clonic seizures

Objective

A number of studies in adults and children with generalized tonic-clonic seizure (GTCS) have reported the alterations in morphometry, functional activity, and functional connectivity (FC) in the thalamus. However, the neural mechanisms underlying the alterations in the thalamus of patients with GTCS are not well understood, particularly in children. The aim of the current study was to explore the temporal properties of functional pathways connecting thalamus in children with GTCS.

Methods

Here, we recruited 24 children with GTCS and 36 age-matched healthy controls. Static and dynamic FC approaches were used to evaluate alterations in the temporal variability of thalamo-cortical networks in children with GTCS. The dynamic effective connectivity (dEC) method was also used to evaluate the directions of the fluctuations in effective connectivity. In addition, the relationships between the dynamic properties and clinical features were assessed.

Results

The static FC analysis presented significantly decreased connectivity patterns between the bilateral thalamus and between the thalamus and right inferior temporal gyrus. The dynamic connectivity analysis found decreased FC variability in the thalamo-cortical network of children with epilepsy. Dynamic EC analyses identified increased connectivity variability from the frontal gyrus to the bilateral thalamus, and decreased connectivity variability from the right thalamus to the left thalamus and from the right thalamus to the right superior parietal lobe. In addition, correlation analysis revealed that both static FC and connectivity temporal variability in the thalamo-cortical network related to the clinical features (epilepsy duration and epilepsy onset time).

Significance

Our findings of both increased and decreased connectivity variability in the thalamo-cortical network imply a dynamic restructuring of the functional pathways connecting the thalamus in children with GTCS. These alterations in static and temporal dynamic pathways connecting the bilateral thalamus may extend our understanding of the neural mechanisms underlying the GTCS in children.

Altered dynamic functional network connectivity within default mode network of epileptic children with generalized tonic-clonic seizures

OBJECTIVE

Generalized tonic-clonic seizures (GTCS) is a group of epileptic disorders characterized by widespread generalized spike-and-waves discharges along with unresponsiveness and convulsions. Abnormal connectivity in the DMN is the common findings in children with generalized epilepsy. However, the neural mechanisms underlying the altered brain connectivity of DMN in children with GTCS remain unclear. The aim of the current study was to explore the temporal properties of functional connectivity states by dynamic functional connectivity (dFC) within the DMN of GTCS children.

METHODS

We collected resting-state functional MRI data from 22 GTCS children and 29 age-matched healthy controls. Sliding window approach and k-mean clustering analysis were applied to analyze the dFC and identify transient states of the DMN. Furthermore, the relationship between the dynamic properties and clinical features was assessed.

RESULTS

The dFC analyses identified two reoccurring states: a more frequent and weak connected state (State 1) and a less frequent and strong connected state (State 2). Relative to the normal control, GTCS children spent more time in State 1 showing weak connections and spent less time in State 2 showing strong connections. Dynamic functional network connectivity strength within the DMN showed both increase and decrease in patient group. In addition, the changes of dynamic metric were found to be correlated with epilepsy duration.

SIGNIFICANT

Our findings imply abnormal interactions and the state dynamics in DMN of the children with GTCS. These disruptions of temporal dynamic in DMN may provide significance for understanding the neural mechanism underlying the GTCS in children and suggest that dFC method can be considered as a valuable tool in children with epilepsy.

Changes in the Shape and Volume of Subcortical Structures in Patients With End-Stage Renal Disease

Introduction: End-stage renal disease (ESRD) typically causes changes in brain structure, and patients with ESRD often experience cognitive and sleep disorders. We aimed to assess the changes in the subcortical structure of patients with ESRD and how they are associated with cognitive and sleep disorders.

Methods: We involved 36 adult patients for maintenance hemodialysis and 35 age- and gender-matched control individuals. All participants underwent neuropsychological examination and 3T magnetic resonance imaging (MRI) to acquire T1 anatomical images. The laboratory blood tests were performed in all patients with ESRD close to the time of the MR examination. We used volumetric and vertex-wise shape analysis approaches to investigate the volumes of 14 subcortical structural (e.g., bilateral accumbens, amygdala, hippocampus, caudate, globus pallidus, putamen, and thalamus) abnormalities in the two groups. Analyses of partial correlations and shape correlations were performed in order to identify the associations between subcortical structure, cognition, and sleep quality in patients with ESRD.

Results: The volumetric analysis showed that compared with the healthy control group, patients with ESRD had less bilateral thalamus (left: p < 0.001; right: p < 0.001), bilateral accumbens (left: p < 0.001; right: p = 0.001), and right amygdala (p = 0.002) volumes. In the vertex-wise shape analysis, patients with ESRD had abnormal regional surface atrophy in the bilateral thalamus, right accumbens, left putamen, and bilateral caudate. Moreover, the Montreal Cognitive Assessment (MoCA) score was associated with volume reduction in the bilateral thalamus (left: Spearman ρ = 0.427, p = 0.009; right: ρ = 0.319, p = 0.018), and the Pittsburgh Sleep Quality Index (PSQI) score was associated with volume reduction in the bilateral accumbens (left: ρ = −0.546, p = 0.001; right: ρ = −0.544, p = 0.001). In vertex-wise shape correlation analysis, there was a positive significant correlation between regional shape deformations on the bilateral thalamus and MoCA score in patients with ESRD.

Conclusion: Our study suggested that patients with ESRD have subcortical structural atrophy, which is related to impaired cognitive performance and sleep disturbances. These findings may help to further understand the underlying neural mechanisms of brain changes in patients with ESRD.

Correlation Between the Functional Connectivity of Basal Forebrain Subregions and Vigilance Dysfunction in Temporal Lobe Epilepsy With and Without Focal to Bilateral Tonic-Clonic Seizure

PURPOSE

Previous research has shown that subcortical brain regions are related to vigilance in temporal lobe epilepsy (TLE). However, it is unknown whether alterations in the function and structure of basal forebrain (BF) subregions are associated with vigilance impairment in distinct kinds of TLE. We aimed to investigate changes in the structure and function BF subregions in TLE patients with and without focal to bilateral tonic-clonic seizures (FBTCS) and associated clinical features.

METHODS

A total of 50 TLE patients (25 without and 25 with FBTCS) and 25 healthy controls (HCs) were enrolled in this study. The structural and functional alterations of BF subregions in TLE were investigated using voxel-based morphometry (VBM) and resting-state functional connectivity (rsFC) analysis. Correlation analyses were utilized to investigate correlations between substantially altered imaging characteristics and clinical data from patients.

RESULTS

FBTCS patients had a lower rsFC between Ch1-3 and the bilateral striatum as well as the left cerebellum posterior lobe than non-FBTCS patients. In comparison to non-FBTCS patients, the rsFC between Ch4 and the bilateral amygdala was also lower in FBTCS patients. Compared to HCs, the TLE patients had reduced rsFC between the BF subregions and the cerebellum, striatum, default mode network, frontal lobe, and occipital lobes. In the FBTCS group, the rsFC between the left Ch1-3 and striatum was positive correlated with the vigilance measures. In the non-FBTCS group, the rsFC between the left Ch4 and striatum was significantly negative correlated with the alertness measure.

CONCLUSION

These results extend current understanding of the pathophysiology of impaired vigilance in TLE and imply that the BF subregions may serve as critical nodes for developing and categorizing TLE biomarkers.

Altered Cerebral Blood Flow is Linked to Disease Duration in Patients with Generalized tonic‒clonic Seizures

PURPOSE

To investigate cerebral blood flow (CBF) characteristics in individuals with generalized tonic‒clonic seizures (GTCS) during the interictal phase using voxel-based analysis of 3D pseudocontinuous arterial spin labeling (PCASL).

PATIENTS AND METHODS

Patients with GTCS (GTCS group) (during the interictal period) and healthy volunteers (control group) underwent head MR imaging with a 3.0T MR scanner with a 3D PCASL sequence. CBF was compared between the two groups. Spearman correlations of CBF in regions of interest (ROIs) in GTCS patients with the duration of disease and age of onset were analyzed and corrected using the false discovery rate (FDR).

RESULTS

Twenty patients with GTCS (GTCS group) and twenty healthy volunteers (control group) were recruited for this study. On 3D PCASL, (1) GTCS patients had lower CBF in the brainstem, right cerebellum, right inferior temporal gyrus, parahippocampal gyrus, superior frontal gyrus, middle frontal gyrus, triangular part of inferior frontal gyrus, left temporal pole of superior temporal gyrus and thalamus and had higher CBF in the bilateral superior parietal gyri, precuneus, precentral gyri, postcentral gyri, and left dorsolateral superior frontal gyrus than controls. (2) The CBF of the right temporal pole of the middle temporal gyrus was negatively correlated with the duration of disease (P_FDRcorrected<0.05), with a correlation coefficient r of -0.7333 and a P_FDRcorrected value of 0.04.

CONCLUSION

Voxel-based analysis of 3D PCASL imaging can be used to sensitively detect brain perfusion differences in GTCS patients. The decrease in CBF in the right temporal pole of the middle temporal gyrus may be associated with disease onset. These findings may offer new perspectives on the pathogenesis of GTCS and the underlying pathophysiological changes associated with perfusion.

BCCT: A GUI Toolkit for Brain Structural Covariance Connectivity Analysis on MATLAB

Brain structural covariance network (SCN) can delineate the brain synchronized alterations in a long-range time period. It has been used in the research of cognition or neuropsychiatric disorders. Recently, causal analysis of structural covariance network (CaSCN), winner-take-all and cortex–subcortex covariance network (WTA-CSSCN), and modulation analysis of structural covariance network (MOD-SCN) have expended the technology breadth of SCN. However, the lack of user-friendly software limited the further application of SCN for the research. In this work, we developed the graphical user interface (GUI) toolkit of brain structural covariance connectivity based on MATLAB platform. The software contained the analysis of SCN, CaSCN, MOD-SCN, and WTA-CSSCN. Also, the group comparison and result-showing modules were included in the software. Furthermore, a simple showing of demo dataset was presented in the work. We hope that the toolkit could help the researchers, especially clinical researchers, to do the brain covariance connectivity analysis in further work more easily.

Substance-Specific and Shared Gray Matter Signatures in Alcohol, Opioid, and Polysubstance Use Disorder

Substance use disorders (SUD) have been shown to be associated with gray matter (GM) loss, particularly in the frontal cortex. However, unclear is to what degree these regional GM alterations are substance-specific or shared across different substances, and if these regional GM alterations are independent of each other or the result of system-level processes at the intrinsic connectivity network level. The T1 weighted MRI data of 65 treated patients with alcohol use disorder (AUD), 27 patients with opioid use disorder (OUD) on maintenance therapy, 21 treated patients with stimulant use disorder comorbid with alcohol use disorder (polysubstance use disorder patients, PSU), and 21 healthy controls were examined via data-driven vertex-wise and voxel-wise GM analyses. Then, structural covariance analyses and open-access fMRI database analyses were used to map the cortical thinning patterns found in the three SUD groups onto intrinsic functional systems. Among AUD and OUD, we identified both common cortical thinning in right anterior brain regions as well as SUD-specific regional GM alterations that were not present in the PSU group. Furthermore, AUD patients had not only the most extended regional thinning but also significantly smaller subcortical structures and cerebellum relative to controls, OUD and PSU individuals. The system-level analyses revealed that AUD and OUD showed cortical thinning in several functional systems. In the AUD group the default mode network was clearly most affected, followed by the salience and executive control networks, whereas the salience and somatomotor network were highlighted as critical for understanding OUD. Structural brain alterations in groups with different SUDs are largely unique in their spatial extent and functional network correlates.