Epilepsy and brain network hubs

Epileptic seizure forecasting with wearable-based nocturnal sleep features

OBJECTIVE

Non-invasive biomarkers have recently shown promise for seizure forecasting in people with epilepsy. In this work, we developed a seizure-day forecasting algorithm based on nocturnal sleep features acquired using a smart shirt.

METHODS

Seventy-eight individuals with epilepsy admitted to the Centre hospitalier de l'Université de Montréal epilepsy monitoring unit wore the Hexoskin biometric smart shirt during their stay. The shirt continuously measures electrocardiography, respiratory, and accelerometry activity. Ten sleep features, including sleep efficiency, sleep latency, sleep duration, time spent in non-rapid eye movement sleep (NREM) and rapid eye movement sleep (REM), wakefulness after sleep onset, average heart and breathing rates, high-frequency heart rate variability, and the number of position changes, were automatically computed using the Hexoskin sleep algorithm. Each night's features were then normalized using a reference night for each patient. A support vector machine classifier was trained for pseudo-prospective seizure-day forecasting, with forecasting horizons of 16- and 24-h to include both diurnal and nocturnal seizures (24-h) or diurnal seizures only (16-h). The algorithm's performance was assessed using a nested leave-one-patient-out cross-validation approach.

RESULTS

Improvement over chance (IoC) performances were achieved for 48.7% and 40% of patients with the 16- and 24-h forecasting horizons, respectively. For patients with IoC performances, the proposed algorithm reached mean IoC, sensitivity and time in warning of 34.3%, 86.0%, and 51.7%, respectively for the 16-h horizon, and 34.2%, 64.4% and 30.2%, respectively, for the 24-h horizon.

SIGNIFICANCE

Smart shirt-based nocturnal sleep analysis holds promise as a non-invasive approach for seizure-day forecasting in a subset of people with epilepsy. Further investigations, particularly in a residential setting with long-term recordings, could pave the way for the development of innovative and practical seizure forecasting devices.

PLAIN LANGUAGE SUMMARY

Seizure forecasting with wearable devices may improve the quality of life of people living with epilepsy who experience unpredictable, recurrent seizures. In this study, we have developed a seizure forecasting algorithm using sleep characteristics obtained from a smart shirt worn at night by a large number of hospitalized patients with epilepsy (78). A daily seizure forecast was generated following each night using machine learning methods. Our results show that around half of people with epilepsy may benefit from such an approach.

Localization of epileptogenic zone based on time-varying effective networks

Surgical resection of the epileptogenic zone (EZ) is an effective method for treating drug-resistant epilepsy. At present, the accuracy of EZ localization needs to be further improved. The characteristics of graph theory based on partial directed coherence networks have been applied to the localization of EZ, but the application of network control theory to effective networks to locate EZ is rarely reported. In this study, the method of partial directed coherence analysis was utilized to construct the time-varying effective brain networks of stereo-electroencephalography (SEEG) signals from 20 seizures in 12 patients. Combined with graph theory and network control theory, the differences in network characteristics between epileptogenic and non-epileptogenic zones during seizures were analyzed. We also used dung beetle optimized support vector machine classification model to evaluate the localization effect of EZ based on brain network characteristics of graph theory and controllability. The results showed that the classification of the average controllability feature was the best, and the area under the receiver operating characteristic (ROC) curve (AUC) was 0.9505, which is 1.32 % and 1.97 % higher than the traditional methods. The AUC value increased to 0.9607 after integrating the average controllability with other features. This study proved the effectiveness of controllability characteristic in identifying the EZ and provided a theoretical basis for the clinical application of network controllability in the EZ.

Antiseizure Medications Normalize Electroencephalographic Functional Connectivity and Power in Children With Benign Epilepsy With Centrotemporal Spikes

BACKGROUND

The decision to treat children with benign epilepsy with centrotemporal spikes (BECTS) using antiseizure medications (ASM) is controversial. Our goal is to compare the effect of ASM treatment on the alteration of electroencephalographic (EEG) functional connectivity and power across four frequency bands in children with BECTS.

METHODS

Children with BECTS with two-year follow-up were retrospectively divided into ASM versus non-ASM groups. The network properties of the EEGs as based on network-based statistic and graph theory were evaluated by the following indices: global efficiency, clustering coefficient, betweenness centrality, and nodal strength in four frequency bands (delta, theta, alpha, and beta). EEG power including absolute power (AP) and relative power (RP) was analyzed in four frequency bands.

RESULTS

In children with BECTS with ASM treatment, there was no significant change in EEG connectivity across all bands before and after two years of ASM. In children with BECTS without ASM treatment, there was a significant increase of global efficiency, clustering coefficient, and nodal strength but not the betweenness centrality in the delta band after two years of follow-up. A decrease in AP in the delta and theta bands and a decrease in RP in the theta band were found in the ASM group after two years of treatment.

CONCLUSIONS

Our results suggest that ASM may play a role in modulating the development of increasing overall brain connectivity and in downregulating overt synaptic activity, but not intrinsic focal connectivity, in the early years of BECTS. The changes in the EEG power indicate that ASM significantly normalized slow-wave band power.

Beyond Stereo-EEG: Is It Worth Combining Stereo-EEG With Other Diagnostic Methods?

SUMMARY

Stereo-EEG is a widely used method to improve the diagnostic precision of presurgical workup in patients with refractory epilepsy. Its ability to detect epileptic activity and identify epileptic networks largely depends on the chosen implantation strategy. Even in an ideal situation, electrodes record activity generated in <10% of the brain and contacts only record from brain tissue in their immediate proximity. In this article, the authors discuss how recording stereo-EEG simultaneously with other diagnostic methods can improve its diagnostic value in clinical and research settings. It can help overcome the limited spatial coverage of intracranial recording and better understand the sources of epileptic activity. Simultaneous scalp EEG is the most widely available method, often used to understand large epileptic networks, seizure propagation, and EEG activity occurring on the contralateral hemisphere. Simultaneous magnetoencephalography allows for more precise source localization and identification of deep sources outside the stereo-EEG coverage. Finally, simultaneous functional MRI can highlight metabolic changes following epileptic activity and help understand the widespread network changes associated with interictal activity. This overview highlights advantages and methodological challenges for all these methods. Clinical use and research applications are presented for each approach.

Altered White-Matter Functional Network in Children with Idiopathic Generalized Epilepsy

RATIONALE AND OBJECTIVES

The white matter (WM) functional network changes offers insights into the potential pathological mechanisms of certain diseases, the alterations of WM functional network in idiopathic generalized epilepsy (IGE) remain unclear. We aimed to explore the topological characteristics changes of WM functional network in childhood IGE using resting-state functional Magnetic resonance imaging (MRI) and T1-weighted images.

METHODS

A total of 84 children (42 IGE and 42 matched healthy controls) were included in this study. Functional and structural MRI data were acquired to construct a WM functional network. Group differences in the global and regional topological characteristics were assessed by graph theory and the correlations with clinical and neuropsychological scores were analyzed. A support vector machine algorithm model was employed to classify individuals with IGE using WM functional connectivity as features, and the model's accuracy was evaluated using leave-one-out cross-validation.

RESULTS

In IGE group, at the network level, the WM functional network exhibited increased assortativity; at the nodal level, 17 nodes presented nodal disturbances in WM functional network, and nodal disturbances of 11 nodes were correlated with cognitive performance scores, disease duration and age of onset. The classification model achieved the 72.6% accuracy, 0.746 area under the curve, 69.1% sensitivity, 76.2% specificity.

CONCLUSION

Our study demonstrated that the WM functional network topological properties changes in childhood IGE, which were associated with cognitive function, and WM functional network may help clinical classification for childhood IGE. These findings provide novel information for understanding the pathogenesis of IGE and suggest that the WM function network might be qualified as potential biomarkers.

Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy

Whole-brain functional connectivity networks (connectomes) have been characterized at different scales in humans using EEG and fMRI. Multimodal epileptic networks have also been investigated, but the relationship between EEG and fMRI defined networks on a whole-brain scale is unclear. A unified multimodal connectome description, mapping healthy and pathological networks would close this knowledge gap. Here, we characterize the spatial correlation between the EEG and fMRI connectomes in right and left temporal lobe epilepsy (rTLE/lTLE). From two centers, we acquired resting-state concurrent EEG-fMRI of 35 healthy controls and 34 TLE patients. EEG-fMRI data was projected into the Desikan brain atlas, and functional connectomes from both modalities were correlated. EEG and fMRI connectomes were moderately correlated. This correlation was increased in rTLE when compared to controls for EEG-delta/theta/alpha/beta. Conversely, multimodal correlation in lTLE was decreased in respect to controls for EEG-beta. While the alteration was global in rTLE, in lTLE it was locally linked to the default mode network. The increased multimodal correlation in rTLE and decreased correlation in lTLE suggests a modality-specific lateralized differential reorganization in TLE, which needs to be considered when comparing results from different modalities. Each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy.

Atypical connectome topography and signal flow in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common pharmaco-resistant epilepsy in adults. While primarily associated with mesiotemporal pathology, recent evidence suggests that brain alterations in TLE extend beyond the paralimbic epicenter and impact macroscale function and cognitive functions, particularly memory. Using connectome-wide manifold learning and generative models of effective connectivity, we examined functional topography and directional signal flow patterns between large-scale neural circuits in TLE at rest. Studying a multisite cohort of 95 patients with TLE and 95 healthy controls, we observed atypical functional topographies in the former group, characterized by reduced differentiation between sensory and transmodal association cortices, with most marked effects in bilateral temporo-limbic and ventromedial prefrontal cortices. These findings were consistent across all study sites, present in left and right lateralized patients, and validated in a subgroup of patients with histopathological validation of mesiotemporal sclerosis and post-surgical seizure freedom. Moreover, they were replicated in an independent cohort of 30 TLE patients and 40 healthy controls. Further analyses demonstrated that reduced differentiation related to decreased functional signal flow into and out of temporolimbic cortical systems and other brain networks. Parallel analyses of structural and diffusion-weighted MRI data revealed that topographic alterations were independent of TLE-related cortical thinning but partially mediated by white matter microstructural changes that radiated away from paralimbic circuits. Finally, we found a strong association between the degree of functional alterations and behavioral markers of memory dysfunction. Our work illustrates the complex landscape of macroscale functional imbalances in TLE, which can serve as intermediate markers bridging microstructural changes and cognitive impairment.

Optimizing Surgical Planning for Epilepsy Patients With Multimodal Neuroimaging and Neurophysiology Integration: A Case Study

SUMMARY

Current preoperative evaluation of epilepsy can be challenging because of the lack of a comprehensive view of the network's dysfunctions. To demonstrate the utility of our multimodal neurophysiology and neuroimaging integration approach in the presurgical evaluation, we present a proof-of-concept for using this approach in a patient with nonlesional frontal lobe epilepsy who underwent two resective surgeries to achieve seizure control. We conducted a post-hoc investigation using four neuroimaging and neurophysiology modalities: diffusion tensor imaging, resting-state functional MRI, and stereoelectroencephalography at rest and during seizures. We computed region-of-interest-based connectivity for each modality and applied betweenness centrality to identify key network hubs across modalities. Our results revealed that despite seizure semiology and stereoelectroencephalography indicating dysfunction in the right orbitofrontal region, the maximum overlap on the hubs across modalities extended to right temporal areas. Notably, the right middle temporal lobe region served as an overlap hub across diffusion tensor imaging, resting-state functional MRI, and rest stereoelectroencephalography networks and was only included in the resected area in the second surgery, which led to long-term seizure control of this patient. Our findings demonstrated that transmodal hubs could help identify key areas related to epileptogenic network. Therefore, this case presents a promising perspective of using a multimodal approach to improve the presurgical evaluation of patients with epilepsy.

Modulation index predicts the effect of ethosuximide on developmental and epileptic encephalopathy with spike-and-wave activation in sleep

PURPOSE

In developmental and epileptic encephalopathy with spike-and-wave activation in sleep (DEE-SWAS), the thalamocortical network is suggested to play an important role in the pathophysiology of the progression from focal epilepsy to DEE-SWAS. Ethosuximide (ESM) exerts effects by blocking T-type calcium channels in thalamic neurons. With the thalamocortical network in mind, we studied the prediction of ESM effectiveness in DEE-SWAS treatment using phase-amplitude coupling (PAC) analysis.

METHODS

We retrospectively enrolled children with DEE-SWAS who had an electroencephalogram (EEG) recorded between January 2009 and September 2022 and were prescribed ESM at Okayama University Hospital. Only patients whose EEG showed continuous spike-and-wave during sleep were included. We extracted 5-min non-rapid eye movement sleep stage N2 segments from EEG recorded before starting ESM. We calculated the modulation index (MI) as the measure of PAC in pair combination comprising one of two fast oscillation types (gamma, 40-80 Hz; ripples, 80-150 Hz) and one of five slow-wave bands (delta, 0.5-1, 1-2, 2-3, and 3-4 Hz; theta, 4-8 Hz), and compared it between ESM responders and non-responders.

RESULTS

We identified 20 children with a diagnosis of DEE-SWAS who took ESM. Fifteen were ESM responders. Regarding gamma oscillations, significant differences were seen only in MI with 0.5-1 Hz slow waves in the frontal pole and occipital regions. Regarding ripples, ESM responders had significantly higher MI in coupling with all slow waves in the frontal pole region, 0.5-1, 3-4, and 4-8 Hz slow waves in the frontal region, 3-4 Hz slow waves in the parietal region, 0.5-1, 2-3, 3-4, and 4-8 Hz slow waves in the occipital region, and 3-4 Hz slow waves in the anterior-temporal region.

SIGNIFICANCE

High MI in a wider area of the brain may represent the epileptic network mediated by the thalamus in DEE-SWAS and may be a predictor of ESM effectiveness.

Modeling a neurological disorder as the result of an operator acting on the brain: A first sketch based on network channel modeling

The brain is a complex network, and diseases can alter its structures and connections between regions. Therefore, we can try to formalize the action of diseases by using operators acting on the brain network. Here, we propose a conceptual model of the brain, seen as a multilayer network, whose intra-lobe interactions are formalized as the diagonal blocks of an adjacency matrix. We propose a general and abstract definition of disease as an operator altering the weights of the connections between neural agglomerates, that is, the elements of the brain matrix. As models, we consider examples from three neurological disorders: epilepsy, Alzheimer-Perusini's disease, and schizophrenia. The alteration of neural connections can be seen as alterations of communication pathways, and thus, they can be described with a new channel model.

Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis

Dynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capture either temporal or spatial properties, such as mean and global efficiency, neglecting the significant information embedded in the fusion of spatial and temporal attributes. In addition, dynamic functional connectivity suffers from the problem of temporal mismatch, i.e., the functional connectivity of different subjects at the same time point cannot be matched. To address these problems, this article introduces a novel feature extraction framework grounded in two-directional two-dimensional principal component analysis. This framework is designed to extract features that integrate both spatial and temporal properties of dynamic functional connectivity. Additionally, we propose to use Fourier transform to extract temporal-invariance properties contained in dynamic functional connectivity. Experimental findings underscore the superior performance of features extracted by this framework in classification experiments compared to features capturing individual properties.

Identification of abnormal closed-loop pathways in patients with MRI-negative pharmacoresistant epilepsy

Epilepsy is a disorder of brain networks, that is usually combined with cognitive and emotional impairment. However, most of the current research on closed-loop pathways in epilepsy is limited to the neuronal level or has focused only on known closed-loop pathways, and studies on abnormalities in closed-loop pathways in epilepsy at the whole-brain network level are lacking. A total of 26 patients with magnetic resonance imaging-negative pharmacoresistant epilepsy (MRIneg-PRE) and 26 healthy controls (HCs) were included in this study. Causal brain networks and temporal-lag brain networks were constructed from resting-state functional MRI data, and the Johnson algorithm was used to identify stable closed-loop pathways. Abnormal closed-loop pathways in the MRIneg-PRE cohort compared with the HC group were identified, and the associations of these pathways with indicators of cognitive and emotional impairments were examined via Pearson correlation analysis. The results revealed that the abnormal stable closed-loop pathways were distributed across the frontal, parietal, and occipital lobes and included altered functional connectivity values both within and between cerebral hemispheres. Four abnormal closed-loop pathways in the occipital lobe were associated with emotional and cognitive impairments. These abnormal pathways may serve as biomarkers for the diagnosis and guidance of individualized treatments for MRIneg-PRE patients.

Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug-resistant epilepsy: A machine learning approach

OBJECTIVE

To deconstruct the epileptogenic networks of patients with drug-resistant epilepsy (DRE) using source functional connectivity (FC) analysis; unveil the FC biomarkers of the epileptogenic zone (EZ); and develop machine learning (ML) models to estimate the EZ using brief interictal electroencephalography (EEG) data.

METHODS

We analyzed scalp EEG from 50 patients with DRE who had surgery. We reconstructed the activity (electrical source imaging [ESI]) of virtual sensors (VSs) across the whole cortex and computed FC separately for epileptiform and non-epileptiform EEG epochs (with or without spikes). In patients with good outcome (Engel 1a), four cortical regions were defined: EZ (resection) and three non-epileptogenic zones (NEZs) in the same and opposite hemispheres. Region-specific FC features in six frequency bands and three spatial ranges (long, short, inner) were compared between regions (Wilcoxon sign-rank). We developed ML classifiers to identify the VSs in the EZ using VS-specific FC features. Cross-validation was performed using good outcome data. Performance was compared with poor outcomes and interictal spike localization.

RESULTS

FC differed between EZ and NEZs (p < .05) during non-epileptiform and epileptiform epochs, showing higher FC in the EZ than its homotopic contralateral NEZ. During epileptiform epochs, the NEZ in the epileptogenic hemisphere showed higher FC than its contralateral NEZ. In good outcome patients, the ML classifiers reached 75% accuracy to the resection (91% sensitivity; 74% specificity; distance from EZ: 38 mm) using epileptiform epochs (gamma and beta frequency bands) and 62% accuracy using broadband non-epileptiform epochs, both outperforming spike localization (accuracy = 47%; p < .05; distance from EZ: 57 mm). Lower performance was seen in poor outcomes.

SIGNIFICANCE

We present an FC approach to extract EZ biomarkers from brief EEG data. Increased FC in various frequencies characterized the EZ during epileptiform and non-epileptiform epochs. FC-based ML models identified the resection better in good than poor outcome patients, demonstrating their potential for presurgical use in pediatric DRE.

Hub overload and failure as a final common pathway in neurological brain network disorders

Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.

Functional brain connectivity in children with focal epilepsy: A systematic review of functional MRI studies

Epilepsy is increasingly recognised as a brain network disorder and many studies have investigated functional connectivity (FC) in children with epilepsy using functional MRI (fMRI). This systematic review of fMRI studies, published up to November 2023, investigated profiles of FC changes and their clinical relevance in children with focal epilepsy compared to healthy controls. A literature search in PubMed and Web of Science yielded 62 articles. We categorised the results into three groups: 1) differences in correlation-based FC between patients and controls; 2) differences in other FC measures between patients and controls; and 3) associations between FC and disease variables (for example, age of onset), cognitive and seizure outcomes. Studies revealed either increased or decreased FC across multiple brain regions in children with focal epilepsy. However, findings lacked consistency: conflicting FC alterations (decreased and increased FC) co-existed within or between brain regions across all focal epilepsy groups. The studies demonstrated overall that 1) interhemispheric connections often displayed abnormal connectivity and 2) connectivity within and between canonical functional networks was decreased, particularly for the default mode network. Focal epilepsy disrupted FC in children both locally (e.g., seizure-onset zones, or within-brain subnetworks) and globally (e.g., whole-brain network architecture). The wide variety of FC study methodologies limits clinical application of the results. Future research should employ longitudinal designs to understand the evolution of brain networks during the disease course and explore the potential of FC biomarkers for predicting cognitive and postsurgical seizure outcomes.

Epileptic tissue localization using graph-based networks in the high frequency oscillation range of intracranial electroencephalography

PURPOSE

High frequency oscillations (HFOs) are an emerging biomarker of epilepsy. However, very few studies have investigated the functional connectivity of interictal iEEG signals in the frequency range of HFOs. Here, we study the corresponding functional networks using graph theory, and we assess their predictive value for automatic electrode classification in a cohort of 20 drug resistant patients.

METHODS

Coherence-based connectivity analysis was performed on the iEEG recordings, and six different local graph measures were computed in both sub-bands of the HFO frequency range (80-250 Hz and 250-500 Hz). Correlation analysis was implemented between the local graph measures and the ripple and fast ripple rates. Finally, the WEKA software was employed for training and testing different predictive models on the aforementioned local graph measures.

RESULTS

The ripple rate was significantly correlated with five out of six local graph measures in the functional network. For fast ripples, their rate was also significantly (but negatively) correlated with most of the local metrics. The results from WEKA showed that the Logistic Regression algorithm was able to classify highly HFO-contaminated electrodes with an accuracy of 82.5 % for ripples and 75.4 % for fast ripples.

CONCLUSION

Functional connectivity networks in the HFO band could represent an alternative to the direct use of distinct HFO events, while also providing important insights about hub epileptic areas that can represent possible surgical targets. Automatic electrode classification through FC-based classifiers can help bypass the burden of manual HFO annotation, providing at the same time similar amount of information about the epileptic tissue.

Uncovering microstructural architecture from histology

Microstructural tissue organization underlies the complex connectivity of the brain and controls properties of connective, muscle, and epithelial tissue. However, discerning microstructural architecture with high resolution for large fields of view remains prohibitive. We address this challenge with computational scattered light imaging (ComSLI), which exploits the anisotropic light scattering of aligned structures. Using a rotating lightsource and a high-resolution camera, ComSLI determines fiber architecture with micrometer resolution from histological sections across preparation and staining protocols. We show complex fiber architecture in brain and non-brain sections, including histological paraffin-embedded sections with various stains, and demonstrate its applicability on animal and human tissue, including disease cases with altered microstructure. ComSLI opens new avenues for investigating fiber architecture in new and archived sections across organisms, tissues, and diseases.

A WORLDWIDE ENIGMA STUDY ON EPILEPSY-RELATED GRAY AND WHITE MATTER COMPROMISE ACROSS THE ADULT LIFESPAN

Objectives

Temporal lobe epilepsy (TLE) is commonly associated with mesiotemporal pathology and widespread alterations of grey and white matter structures. Evidence supports a progressive condition although the temporal evolution of TLE is poorly defined. This ENIGMA-Epilepsy study utilized multimodal magnetic resonance imaging (MRI) data to investigate structural alterations in TLE patients across the adult lifespan. We charted both grey and white matter changes and explored the covariance of age-related alterations in both compartments.

Methods

We studied 769 TLE patients and 885 healthy controls across an age range of 17-73 years, from multiple international sites. To assess potentially non-linear lifespan changes in TLE, we harmonized data and combined median split assessments with cross-sectional sliding window analyses of grey and white matter age-related changes. Covariance analyses examined the coupling of grey and white matter lifespan curves.

Results

In TLE, age was associated with a robust grey matter thickness/volume decline across a broad cortico-subcortical territory, extending beyond the mesiotemporal disease epicentre. White matter changes were also widespread across multiple tracts with peak effects in temporo-limbic fibers. While changes spanned the adult time window, changes accelerated in cortical thickness, subcortical volume, and fractional anisotropy (all decreased), and mean diffusivity (increased) after age 55 years. Covariance analyses revealed strong limbic associations between white matter tracts and subcortical structures with cortical regions.

Conclusions

This study highlights the profound impact of TLE on lifespan changes in grey and white matter structures, with an acceleration of aging-related processes in later decades of life. Our findings motivate future longitudinal studies across the lifespan and emphasize the importance of prompt diagnosis as well as intervention in patients.

Does the brain behave like a (complex) network? I. Dynamics

Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network structure does not entail that the brain actually works as a network. Asking whether the brain behaves as a network means asking whether network properties count. From the viewpoint of neurophysiology and, possibly, of brain physics, the most substantial issues a network structure may be instrumental in addressing relate to the influence of network properties on brain dynamics and to whether these properties ultimately explain some aspects of brain function. Here, we address the dynamical implications of complex network, examining which aspects and scales of brain activity may be understood to genuinely behave as a network. To do so, we first define the meaning of networkness, and analyse some of its implications. We then examine ways in which brain anatomy and dynamics can be endowed with a network structure and discuss possible ways in which network structure may be shown to represent a genuine organisational principle of brain activity, rather than just a convenient description of its anatomy and dynamics.

Altered static and dynamic functional connectivity of the default mode network across epilepsy subtypes in children: A resting-state fMRI study

BACKGROUND

Epilepsy is a chronic neurologic disorder characterized by abnormal functioning of brain networks, making it a complex research topic. Recent advancements in neuroimaging technology offer an effective approach to unraveling the intricacies of the human brain. Within different types of epilepsy, there is growing recognition regarding ongoing changes in the default mode network (DMN). However, little is known about the shared and distinct alterations of static functional connectivity (sFC) and dynamic functional connectivity (dFC) in DMN among epileptic subtypes, especially in children with epilepsy.

METHODS

Here, 110 children with epilepsy at a single center, including idiopathic generalized epilepsy (IGE), frontal lobe epilepsy (FLE), temporal lobe epilepsy (TLE), and parietal lobe epilepsy (PLE), as well as 84 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (fMRI) scan. We investigated both sFC and dFC between groups of the DMN.

RESULTS

Decreased static and dynamic connectivity within the DMN subsystem were shared by all subtypes. In each epilepsy subtype, children with epilepsy displayed significant and distinct patterns of DMN connectivity compared to the control group: the IGE group showed reduced interhemispheric connectivity, the FLE group consistently demonstrated disturbances in frontal region connectivity, the TLE group exhibited significant disruptions in hippocampal connectivity, and the PLE group displayed a notable decrease in parietal-temporal connectivity within the DMN. Some state-specific FC disruptions (decreased dFC) were observed in each epilepsy subtype that cannot detect by sFC. To determine their uniqueness within specific subtypes, bootstrapping methods were employed and found the significant results (IGE: between PCC and bilateral precuneus, FLE: between right middle frontal gyrus and bilateral middle temporal gyrus, TLE: between left Hippocampus and right fusiform, PLE: between left angular and cingulate cortex). Furthermore, only children with IGE exhibited dynamic features associated with clinical variables.

CONCLUSIONS

Our findings highlight both shared and distinct FC alterations within the DMN in children with different types of epilepsy. Furthermore, our work provides a novel perspective on the functional alterations in the DMN of pediatric patients, suggesting that combined sFC and dFC analysis can provide valuable insights for deepening our understanding of the neuronal mechanism underlying epilepsy in children.

Microglia in Microbiota-Gut-Brain Axis: A Hub in Epilepsy

There is growing concern about the role of the microbiota-gut-brain axis in neurological illnesses, and it makes sense to consider microglia as a critical component of this axis in the context of epilepsy. Microglia, which reside in the central nervous system, are dynamic guardians that monitor brain homeostasis. Microglia receive information from the gut microbiota and function as hubs that may be involved in triggering epileptic seizures. Vagus nerve bridges the communication in the axis. Essential axis signaling molecules, such as gamma-aminobutyric acid, 5-hydroxytryptamin, and short-chain fatty acids, are currently under investigation for their participation in drug-resistant epilepsy (DRE). In this review, we explain how vagus nerve connects the gut microbiota to microglia in the brain and discuss the emerging concepts derived from this interaction. Understanding microbiota-gut-brain axis in epilepsy brings hope for DRE therapies. Future treatments can focus on the modulatory effect of the axis and target microglia in solving DRE.

Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in focal epilepsy

OBJECTIVE

We aimed to develop a new approach for identifying the localization of the seizure onset zone (SOZ) based on corticocortical evoked potentials (CCEPs) and to compare the connectivity patterns in patients with different clinical phenotypes.

METHODS

Fifty patients who underwent stereoelectroencephalography and CCEP procedures were included. Logistic regression was used in the model, and six CCEP metrics were input as features: root mean square of the first peak (N1RMS) and second peak (N2RMS), peak latency, onset latency, width duration, and area.

RESULTS

The area under the curve (AUC) for localizing the SOZ ranged from 0.88 to 0.93. The N1RMS values in the hippocampus sclerosis (HS) group were greater than that of the focal cortical dysplasia (FCD) IIa group (p < 0.001), independent of the distance between the recorded and stimulated sites. The sensitivity of localization was higher in the seizure-free group than in the non-seizure-free group (p = 0.036).

CONCLUSIONS

This new method can be used to predict the SOZ localization in various focal epilepsy phenotypes.

SIGNIFICANCE

This study proposed a machine-learning approach for localizing the SOZ. Moreover, we examined how clinical phenotypes impact large-scale abnormality of the epileptogenic networks.

EEG-based spatiotemporal dynamics of fast ripple networks and hubs in infantile epileptic spasms

OBJECTIVE

Infantile epileptic spasms (IS) are epileptic seizures that are associated with increased risk for developmental impairments, adult epilepsies, and mortality. Here, we investigated coherence-based network dynamics in scalp EEG of infants with IS to identify frequency-dependent networks associated with spasms. We hypothesized that there is a network of increased fast ripple connectivity during the electrographic onset of clinical spasms, which is distinct from controls.

METHODS

We retrospectively analyzed peri-ictal and interictal EEG recordings of 14 IS patients. The data was compared with 9 age-matched controls. Wavelet phase coherence (WPC) was computed between 0.2 and 400 Hz. Frequency- and time-dependent brain networks were constructed using this coherence as the strength of connection between two EEG channels, based on graph theory principles. Connectivity was evaluated through global efficiency (GE) and channel-based closeness centrality (CC), over frequency and time.

RESULTS

GE in the fast ripple band (251-400 Hz) was significantly greater following the onset of spasms in all patients (P < 0.05). Fast ripple networks during the first 10s from spasm onset show enhanced anteroposterior gradient in connectivity (posterior > central > anterior, Kruskal-Wallis P < 0.001), with maximum CC over the centroparietal channels in 10/14 patients. Additionally, this anteroposterior gradient in CC connectivity is observed during spasms but not during the interictal awake or asleep states of infants with IS. In controls, anteroposterior gradient in fast ripple CC was noted during arousals and wakefulness but not during sleep. There was also a simultaneous decrease in GE in the 5-8 Hz range after the onset of spasms (P < 0.05), of unclear biological significance.

SIGNIFICANCE

We identified an anteroposterior gradient in the CC connectivity of fast ripple hubs during spasms. This anteroposterior gradient observed during spasms is similar to the anteroposterior gradient in the CC connectivity observed in wakefulness or arousals in controls, suggesting that this state change is related to arousal networks.

Structural and functional changes in the default mode network in drug-resistant epilepsy

PURPOSE

To investigate brain network properties and connectivity abnormalities of the default mode network (DMN) in drug-resistant epilepsy (DRE). The study was based on probabilistic fiber tracking and functional connectivity (FC) analysis, to explore the structural and functional connectivity patterns change between frontal lobe epilepsy (FLE) and temporal lobe epilepsy (TLE).

METHODS

A total of 33 DRE patients (18 TLE and 15 FLE) and 30 healthy controls (HCs) were recruited. The volume fraction of the septal brain region of the DMN in DRE was calculated using FreeSurfer. The FC analysis was performed using Data Processing and Analysis for Brain Imaging in MATLAB. The structural connections between brain regions of the DMN were calculated based on probabilistic fiber tracking.

RESULTS

The left precuneus (PCUN) volumes in epilepsy groups were lower than that in HCs. Compared with FLE, TLE showed reduced FC between the left hippocampus (HIP) and PCUN/medial frontal gyrus, and between the right inferior parietal lobule (IPL) and right superior temporal gyrus. Compared with HCs, FLE showed increased FCs between the right IPL and occipital lobe, and between the left superior frontal gyrus (SFG) and bilateral superior temporal gyrus. In terms of structural connectivity, TLE exhibited increased connectivity strength between the left SFG and left PCUN, and showed reduced connection strength between the left HIP and left posterior cingulate gyrus/left PCUN, when compared with the FLE.

CONCLUSIONS

TLE and FLE patients showed structural and functional changes in the DMN. Compared with FLE patients, the TLE patients showed reduced structural and functional connection strengths between the left HIP and PCUN. These alterations in connection strengths holds promise for the identification of TLE and FLE.

Thyroid function and epilepsy: a two-sample Mendelian randomization study

Background

Thyroid hormones (THs) play a crucial role in regulating various biological processes, particularly the normal development and functioning of the central nervous system (CNS). Epilepsy is a prevalent neurological disorder with multiple etiologies. Further in-depth research on the role of thyroid hormones in epilepsy is warranted.

Methods

Genome-wide association study (GWAS) data for thyroid function and epilepsy were obtained from the ThyroidOmics Consortium and the International League Against Epilepsy (ILAE) Consortium cohort, respectively. A total of five indicators of thyroid function and ten types of epilepsy were included in the analysis. Two-sample Mendelian randomization (MR) analyses were conducted to investigate potential causal relations between thyroid functions and various epilepsies. Multiple testing correction was performed using Bonferroni correction. Heterogeneity was calculated with the Cochran's Q statistic test. Horizontal pleiotropy was evaluated by the MR-Egger regression intercept. The sensitivity was also examined by leave-one-out strategy.

Results

The findings indicated the absence of any causal relationship between abnormalities in thyroid hormone and various types of epilepsy. The study analyzed the odds ratio (OR) between thyroid hormones and various types of epilepsy in five scenarios, including free thyroxine (FT4) on focal epilepsy with hippocampal sclerosis (IVW, OR = 0.9838, p = 0.02223), hyperthyroidism on juvenile absence epilepsy (IVW, OR = 0.9952, p = 0.03777), hypothyroidism on focal epilepsy with hippocampal sclerosis (IVW, OR = 1.0075, p = 0.01951), autoimmune thyroid diseases (AITDs) on generalized epilepsy in all documented cases (weighted mode, OR = 1.0846, p = 0.0346) and on childhood absence epilepsy (IVW, OR = 1.0050, p = 0.04555). After Bonferroni correction, none of the above results showed statistically significant differences.

Conclusion

This study indicates that there is no causal relationship between thyroid-related disorders and various types of epilepsy. Future research should aim to avoid potential confounding factors that might impact the study.

The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives

Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.

Deep Brain Stimulation for Children with Generalized Epilepsy

Intracranial neuromodulation is an evolving therapy for patients with drug-resistant epilepsy (DRE). Deep brain stimulation (DBS) is now available as a therapy for patients with DRE and focal-onset seizures in select health care systems; however, there remains a substantial need of efficacy data before DBS can be more widely adopted into routine clinical practice. This review and commentary focuses on a particular shifting paradigm: DBS as a therapy for children with generalized-onset seizures.

Imaging biomarkers of sleep-related hypermotor epilepsy and sudden unexpected death in epilepsy: a review

In recent years, imaging has emerged as a promising source of several intriguing biomarkers in epilepsy, due to the impressive growth of imaging technology, supported by methodological advances and integrations of post-processing techniques. Bearing in mind the mutually influencing connection between sleep and epilepsy, we focused on sleep-related hypermotor epilepsy (SHE) and sudden unexpected death in epilepsy (SUDEP), aiming to make order and clarify possible clinical utility of emerging multimodal imaging biomarkers of these two epilepsy-related entities commonly occurring during sleep. Regarding SHE, advanced structural techniques might soon emerge as a promising source of diagnostic and predictive biomarkers, tailoring a targeted therapeutic (surgical) approach for MRI-negative subjects. Functional and metabolic imaging may instead unveil SHE's extensive and night-related altered brain networks, providing insights into distinctions and similarities with non-epileptic sleep phenomena, such as parasomnias. SUDEP is considered a storm that strikes without warning signals, but objective subtle structural and functional alterations in autonomic, cardiorespiratory, and arousal centers are present in patients eventually experiencing SUDEP. These alterations could be seen both as susceptibility and diagnostic biomarkers of the underlying pathological ongoing loop ultimately ending in death. Finally, given that SHE and SUDEP are rare phenomena, most evidence on the topic is derived from small single-center experiences with scarcely comparable results, hampering the possibility of performing any meta-analytic approach. Multicenter, longitudinal, well-designed studies are strongly encouraged.

Effect of intracranial electrical stimulation on dynamic functional connectivity in medically refractory epilepsy

Objective

The objective of this study was to explore the distributed network effects of intracranial electrical stimulation in patients with medically refractory epilepsy using dynamic functional connectivity (dFC) and graph indicators.

Methods

The time-varying connectivity patterns of dFC (state-based metrics) as well as topological properties of static functional connectivity (sFC) and dFC (graph indicators) were assessed before and after the intracranial electrical stimulation. The sliding window method and k-means clustering were used for the analysis of dFC states, which were characterized by connectivity strength, occupancy rate, dwell time, and transition. Graph indicators for sFC and dFC were obtained using group statistical tests.

Results

DFCs were clustered into two connectivity configurations: a strongly connected state (state 1) and a sparsely connected state (state 2). After electrical stimulation, the dwell time and occupancy rate of state 1 decreased, while that of state 2 increased. Connectivity strengths of both state 1 and state 2 decreased. For graph indicators, the clustering coefficient, k-core, global efficiency, and local efficiency of patients showed a significant decrease, but the brain networks of patients exhibited higher modularity after electrical stimulation. Especially, for state 1, there was a significant decrease in functional connectivity strength after stimulation within and between the frontal lobe and temporary lobe, both of which are associated with the seizure onset.

Conclusion

Our findings demonstrated that intracranial electrical stimulation significantly changed the time-varying connectivity patterns and graph indicators of the brain in patients with medically refractory epilepsy. Specifically, the electrical stimulation decreased functional connectivity strength in both local-level and global-level networks. This might provide a mechanism of understanding for the distributed network effects of intracranial electrical stimulation and extend the knowledge of the pathophysiological network of medically refractory epilepsy.

Abnormal functional connectivity strength in age-related macular degeneration patients: a fMRI study

OBJECTIVE

Age-related macular degeneration (AMD) is a serious blinding eye disease. Previous neuroimaging studies reported that AMD were accompanied by abnormalities of the brain. However, whether AMD patients were associated with functional connectivity strength (FCS) or not remains unknown. In our study, the purpose of the study was to assess FCS changes in AMD patients.

METHODS

In our study, 20 AMD patients and 20 healthy controls (HCs), matched closely by sex, age, and educational level were underwent MRI scanning. FCS method and seed-based functional connectivity (FC) method were applied to investigate the functional network changes between two groups. Moreover, support vector machine (SVM) method was applied to assess the FCS maps as a feature to classification of AMD diseases.

RESULTS

Our study reported that AMD patients showed decreased FCS values in the bilateral calcarine, left supplementary motor area, left superior parietal lobule and left paracentral lobule (ParaL) relative to the HC group. Meanwhile, our study found that the AMD patients showed abnormal FC within visual network, sensorimotor network and default mode network. Moreover, the SVM method showed that FCS maps as machine learning features shows good classification efficiency (area under curve = 0.82) in the study.

CONCLUSION

Our study demonstrated that AMD patients showed abnormal FCS with the visual network, sensorimotor network and default mode network, which might reflect the impaired vision, cognition and motor function in AMD patients. In addition, FCS indicator can be used as an effective biological marker to assist the clinical diagnosis of AMD.

Anti-inflammatory effects of icariin in the acute and chronic phases of the mouse pilocarpine model of epilepsy

Neuroinflammation mediated by microglia made a significant contribution in the pathophysiology of epilepsy. Icariin (ICA), a bioactive ingredient isolated from Epimedium, has been shown to present both antioxidant and anti-inflammatory properties. This study was to explore the potential therapeutic effects of icariin on mouse pilocarpine model of epilepsy and its underlying mechanisms in vivo and in vitro. To this end, we firstly measured the serum concentrations of the proinflammatory cytokines IL-1β and IL-6 from patients with temporal lobe epilepsy and found that patients with a higher seizure frequency showed correspondingly higher inflammatory reaction. Mouse pharmacokinetic study, transmembrane transportation assay, and cell viability assay collectively demonstrated that ICA was able to cross the blood-brain barrier and has good biocompatibility. The acute and chronic epilepsy models were next established in a pilocarpine mouse model of acquired epilepsy. Icariin has been identified that it could cross the blood-brain barrier and enter the hippocampus to exhibit therapeutic effects. ICA treatment dramatically promoted microglial polarization to the M2 phenotype in epilepsy mice both in the acute and chronic phases. Reduced release of M1-associated proinflammatory factors, such as IL-1β and IL-6, corroborates the altered glial cell polarization. Furthermore, ICA alleviated seizure intensity and mortality in acute phase epileptic mice. Models in the chronic group also showed improved general condition, cognition ability, and memory function after ICA treatment. Taken together, our research strongly suggested that icariin has the potential to treat epilepsy via inhibiting neuroinflammation by promoting microglial polarization to the M2 phenotype.

A scoping review of the functional magnetic resonance imaging-based functional connectivity of focal cortical dysplasia-related epilepsy

Focal cortical dysplasia (FCD) is the most frequent etiology of operable pharmacoresistant epilepsy in children. There is burgeoning evidence that FCD-related epilepsy is a disorder that involves distributed brain networks. Functional magnetic resonance imaging (fMRI) is a tool that allows one to infer neuronal activity and to noninvasively map whole-brain functional networks. Despite its relatively widespread availability at most epilepsy centers, the clinical application of fMRI remains mostly task-based in epilepsy. Another approach is to map and characterize cortical functional networks of individuals using resting state fMRI (rsfMRI). The focus of this scoping review is to summarize the evidence to date of investigations of the network basis of FCD-related epilepsy, and to highlight numerous potential future applications of rsfMRI in the exploration of diagnostic and therapeutic strategies for FCD-related epilepsy. There are numerous studies demonstrating a global disruption of cortical functional networks in FCD-related epilepsy. The underlying pathological subtypes of FCD influence overall functional network patterns. There is evidence that cortical functional network mapping may help to predict postsurgical seizure outcomes, highlighting the translational potential of these findings. Additionally, several studies emphasize the important effect of FCD interaction with cortical networks and the expression of epilepsy and its comorbidities.

From basic sciences and engineering to epileptology: A translational approach

Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal-processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)-enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data-sharing initiatives.

Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference

Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics.

Brain functional connectivity-based prediction of vagus nerve stimulation efficacy in pediatric pharmacoresistant epilepsy

OBJECTIVE

Although vagus nerve stimulation (VNS) is a common and widely used therapy for pharmacoresistant epilepsy, the reported efficacy of VNS in pediatric patients varies, so it is unclear which children will respond to VNS therapy. This study aimed to identify functional brain network features associated with VNS action to distinguish VNS responders from nonresponders using scalp electroencephalogram (EEG) data.

METHODS

Twenty-three children were included in this study, 16 in the discovery cohort and 7 in the test cohort. Using partial correlation value as a measure of whole-brain functional connectivity, we identified the differential edges between responders and nonresponders. Results derived from this were used as input to generate a support vector machine-learning classifier to predict VNS outcomes.

RESULTS

The postcentral gyrus in the left and right parietal lobe regions was identified as the most significant differential brain region between VNS responders and nonresponders (p < 0.001). The resultant classifier demonstrated a mean AUC value of 0.88, a mean sensitivity rate of 91.4%, and a mean specificity rate of 84.3% on fivefold cross-validation in the discovery cohort. In the testing cohort, our study demonstrated an AUC value of 0.91, a sensitivity rate of 86.6%, and a specificity rate of 79.3%. Furthermore, for prediction accuracy, our model can achieve 81.4% accuracy at the epoch level and 100% accuracy at the patient level.

SIGNIFICANCE

This study provides the first treatment response prediction model for VNS using scalp EEG data with ictal recordings and offers new insights into its mechanism of action. Our results suggest that brain functional connectivity features can help predict therapeutic response to VNS therapy. With further validation, our model could facilitate the selection of targeted pediatric patients and help avoid risky and costly procedures for patients who are unlikely to benefit from VNS therapy.

Limbic network co-localization predicts pharmacoresistance in dysplasia-related epilepsy

To evaluate the role of focal cortical dysplasia co-localization to cortical functional networks in the development of pharmacoresistance. One hundred thirty-six focal cortical dysplasia patients with 3.0 T or 1.5 T MRI were identified from clinical databases at Children's National Hospital. Clinico-radio-pathologic factors and network co-localization were determined. Using binomial logistic regression, limbic network co-localization (odds ratio 4.164 95% confidence interval 1.02-17.08, p = 0.048), and focal to bilateral tonic-clonic seizures (4.82, 1.30-18.03, p = 0.019) predicted pharmacoresistance. These findings provide clinicians with markers to identify patients with focal cortical dysplasia-related epilepsy at high risk of developing pharmacoresistance and should facilitate earlier epilepsy surgical evaluation.

The evolution of antiseizure medication therapy selection in adults: Is artificial intelligence -assisted antiseizure medication selection ready for prime time?

Antiseizure medications (ASMs) are the mainstay of symptomatic epilepsy treatment. The primary goal of pharmacotherapy with ASMs in epilepsy is to achieve complete seizure remission while minimizing therapy-related adverse events. Over the years, more ASMs have been introduced, with approximately 30 now in everyday use. With such a wide variety, much guidance is needed in choosing ASMs for initial therapy, subsequent replacement monotherapy, or adjunctive therapy. The specific ASMs are typically tailored by the patient's related factors, including epilepsy syndrome, age, sex, comorbidities, and ASM characteristics, including the spectrum of efficacy, pharmacokinetic properties, safety, and tolerability. Weighing these key clinical variables requires experience and expertise that may be limited. Furthermore, with this approach, patients may endure multiple trials of ineffective treatments before the most appropriate ASM is found. A more reliable way to predict response to different ASMs is needed so that the most effective and tolerated ASM can be selected. Soon, alternative approaches, such as deep machine learning (ML), could aid the individualized selection of the first and subsequent ASMs. The recognition of epilepsy as a network disorder and the integration of personalized epilepsy networks in future ML platforms can also facilitate the prediction of ASM response. Augmenting the conventional approach with artificial intelligence (AI) opens the door to personalized pharmacotherapy in epilepsy. However, more work is needed before these models are ready for primetime clinical practice.

Disruptions in modular structure and network integration of language-related network predict language performance in temporal lobe epilepsy: Evidence from graph-based analysis

OBJECTIVE

Temporal lobe epilepsy (TLE) is a network disorder that alters the total organization of the language-related network. Task-based functional magnetic resonance imaging (fMRI) aimed at functional connectivity is a direct method to investigate how the network is reorganized. However, such studies are scarce and represented mostly by the resting-state analysis of the individual connections between regions. To fill this gap, we used a graph-based analysis, which allows us to cover the total language-related network changes, such as disruptions in an integration/segregation balance, during a language task in TLE.

METHODS

We collected task-based fMRI data with sentence completion from 19 healthy controls and 28 people with left TLE. Using graph-based analysis, we estimated how the language-related network segregated into modules and tested whether they differed between groups. We evaluated the total network integration and the integration within modules. To assess intermodular integration, we considered the number and location of connector hubs-regions with high connectivity.

RESULTS

The language-related network was differently segregated during language processing in the groups. While healthy controls showed a module consisting of left perisylvian regions, people with TLE exhibited a bilateral module formed by the anterior language-related areas and a module in the left temporal lobe, reflecting hyperconnectivity within the epileptic focus. As a consequence of this reorganization, there was a statistical tendency that the dominance of the intramodular integration over the total network integration was greater in TLE, which predicted language performance. The increase in the number of connector hubs in the right hemisphere, in turn, was compensatory in TLE.

SIGNIFICANCE

Our study provides insights into the reorganization of the language-related network in TLE, revealing specific network changes in segregation and integration. It confirms reduced global connectivity and compensation across the healthy hemisphere, commonly observed in epilepsy. These findings advance the understanding of the network-based reorganizational processes underlying language processing in TLE.

Cortical microstructural gradients capture memory network reorganization in temporal lobe epilepsy

Temporal lobe epilepsy (TLE), one of the most common pharmaco-resistant epilepsies, is associated with pathology of paralimbic brain regions, particularly in the mesiotemporal lobe. Cognitive dysfunction in TLE is frequent, and particularly affects episodic memory. Crucially, these difficulties challenge the quality of life of patients, sometimes more than seizures, underscoring the need to assess neural processes of cognitive dysfunction in TLE to improve patient management. Our work harnessed a novel conceptual and analytical approach to assess spatial gradients of microstructural differentiation between cortical areas based on high-resolution MRI analysis. Gradients track region-to-region variations in intracortical lamination and myeloarchitecture, serving as a system-level measure of structural and functional reorganization. Comparing cortex-wide microstructural gradients between 21 patients and 35 healthy controls, we observed a reorganization of this gradient in TLE driven by reduced microstructural differentiation between paralimbic cortices and the remaining cortex with marked abnormalities in ipsilateral temporopolar and dorsolateral prefrontal regions. Findings were replicated in an independent cohort. Using an independent post-mortem dataset, we observed that in vivo findings reflected topographical variations in cortical cytoarchitecture. We indeed found that macroscale changes in microstructural differentiation in TLE reflected increased similarity of paralimbic and primary sensory/motor regions. Disease-related transcriptomics could furthermore show specificity of our findings to TLE over other common epilepsy syndromes. Finally, microstructural dedifferentiation was associated with cognitive network reorganization seen during an episodic memory functional MRI paradigm and correlated with interindividual differences in task accuracy. Collectively, our findings showing a pattern of reduced microarchitectural differentiation between paralimbic regions and the remaining cortex provide a structurally-grounded explanation for large-scale functional network reorganization and cognitive dysfunction characteristic of TLE.

Abnormal static and dynamic functional network connectivity of the whole-brain in children with generalized tonic-clonic seizures

Introduction

Generalized tonic-clonic seizures (GTCS) are a subtype of generalized seizures exhibiting bursts of bilaterally synchronous generalized spike-wave discharges. Numerous neuroimaging studies have reported aberrant functional activity and topological organization of brain network in epilepsy patients with GTCS, but most studies have focused on adults. However, the effect of GTCS on the spatial and temporal properties of brain function in children remains unclear. The present study aimed to explore whole-brain static (sFC) and dynamic functional connectivity (dFC) in children with GTCS.

Methods

Twenty-three children with GTCS and 32 matched healthy controls (HCs) were recruited for the present study. Resting-state functional magnetic resonance imaging (MRI) data were collected for each subject. The group independent component analysis method was used to obtain independent components (ICs). Then, sFC and dFC methods were applied and the differences in functional connectivity (FC) were compared between the children with GTCS and the HCs. Additionally, we investigated the correlations between the dFC indicators and epilepsy duration.

Results

Compared to HCs, GTCS patients exhibited a significant decrease in sFC strengths among most networks. The K-means clustering method was implemented for dFC analysis, and the optimal number of clusters was estimated: two discrete connectivity configurations, State 1 (strong connection) and State 2 (weak connection). The decreased dFC mainly occurred in State 1, especially the dFC between the visual network (VIS) and somatomotor network (SMN); but the increased dFC mainly occurred in State 2 among most networks in GTCS children. In addition, GTCS children showed significantly shorter mean dwell time and lower fractional windows in stronger connected State 1, while GTCS children showed significantly longer mean dwell time in weaker connected State 2. In addition, the dFC properties, including mean dwell time and fractional windows, were significantly correlated with epilepsy duration.

Conclusion

Our results indicated that GTCS epilepsy not only alters the connectivity strength but also changes the temporal properties of connectivity in networks in the whole brain. These findings also emphasized the differences in sFC and dFC in children with GTCS. Combining sFC and dFC methods may provide more comprehensive understanding of the abnormal changes in brain architecture in children with GTCS.

Structural brain network analysis in occipital lobe epilepsy

BACKGROUND

This study aimed to analyze the structural brain network in patients with occipital lobe epilepsy (OLE) and investigate the differences in structural brain networks between patients with OLE and healthy controls.

METHODS

Patients with OLE and healthy controls with normal brain MRI findings were enrolled. They underwent diffusion tensor imaging using a 3.0T MRI scanner, and we computed the network measures of global and local structural networks in patients with OLE and healthy controls using the DSI studio program. We compared network measures between the groups.

RESULTS

We enrolled 23 patients with OLE and 42 healthy controls. There were significant differences in the global structural network between patients with OLE and healthy controls. The assortativity coefficient (-0.0864 vs. -0.0814, p = 0.0214), mean clustering coefficient (0.0061 vs. 0.0064, p = 0.0203), global efficiency (0.0315 vs. 0.0353, p = 0.0086), and small-worldness index (0.0001 vs. 0.0001, p = 0.0175) were lower, whereas the characteristic path length (59.2724 vs. 53.4684, p = 0.0120) was higher in patients with OLE than those in the healthy controls. There were several nodes beyond the occipital lobe that showed significant differences in the local structural network between the groups. In addition, the assortativity coefficient was negatively correlated with the duration of epilepsy (r=-0.676, p = 0.001).

The Interictal Suppression Hypothesis in focal epilepsy: network-level supporting evidence

Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10-13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10-3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10-12). Structure-function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10-21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.

An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application

Epilepsy surgery continues to be a recommended treatment for intractable (medication-resistant) epilepsy; however, 30-70% of epilepsy surgery patients can continue to have seizures. Surgical failures are often associated with incomplete resection or inaccurate localization of the epileptogenic zone. This retrospective study aims to improve surgical outcome through in silico testing of surgical hypotheses through a personalized computational neurosurgery model created from individualized patient's magnetoencephalography recording and MRI. The framework assesses the extent of the epileptic network and evaluates underlying spike dynamics, resulting in identification of one single brain volume as a candidate for resection. Dynamic-locked networks were utilized for virtual cortical resection. This in silico protocol was tested in a cohort of 24 paediatric patients with focal drug-resistant epilepsy who underwent epilepsy surgery. Of 24 patients who were included in the analysis, 79% (19 of 24) of the models agreed with the patient's clinical surgery outcome and 21% (5 of 24) were considered as model failures (accuracy 0.79, sensitivity 0.77, specificity 0.82). Patients with unsuccessful surgery outcome typically showed a model cluster outside of the resected cavity, while those with successful surgery showed the cluster model within the cavity. Two of the model failures showed the cluster in the vicinity of the resected tissue and either a functional disconnection or lack of precision of the magnetoencephalography-MRI overlapping could explain the results. Two other cases were seizure free for 1 year but developed late recurrence. This is the first study that provides in silico personalized protocol for epilepsy surgery planning using magnetoencephalography spike network analysis. This model could provide complementary information to the traditional pre-surgical assessment methods and increase the proportion of patients achieving seizure-free outcome from surgery.

Atypical connectome topography and signal flow in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is one of the most common pharmaco-resistant epilepsies in adults. While hippocampal pathology is the hallmark of this condition, emerging evidence indicates that brain alterations extend beyond the mesiotemporal epicenter and affect macroscale brain function and cognition. We studied macroscale functional reorganization in TLE, explored structural substrates, and examined cognitive associations. We investigated a multisite cohort of 95 patients with pharmaco-resistant TLE and 95 healthy controls using state-of-the-art multimodal 3T magnetic resonance imaging (MRI). We quantified macroscale functional topographic organization using connectome dimensionality reduction techniques and estimated directional functional flow using generative models of effective connectivity. We observed atypical functional topographies in patients with TLE relative to controls, manifesting as reduced functional differentiation between sensory/motor networks and transmodal systems such as the default mode network, with peak alterations in bilateral temporal and ventromedial prefrontal cortices. TLE-related topographic changes were consistent in all three included sites and reflected reductions in hierarchical flow patterns between cortical systems. Integration of parallel multimodal MRI data indicated that these findings were independent of TLE-related cortical grey matter atrophy, but mediated by microstructural alterations in the superficial white matter immediately beneath the cortex. The magnitude of functional perturbations was robustly associated with behavioral markers of memory function. Overall, this work provides converging evidence for macroscale functional imbalances, contributing microstructural alterations, and their associations with cognitive dysfunction in TLE.

Exploring the therapeutic and anti-tumor properties of morusin: a review of recent advances

Morusin is a natural product that has been isolated from the bark of Morus alba, a species of mulberry tree. It belongs to the flavonoid family of chemicals, which is abundantly present in the plant world and is recognized for its wide range of biological activities. Morusin has a number of biological characteristics, including anti-inflammatory, anti-microbial, neuro-protective, and antioxidant capabilities. Morusin has exhibited anti-tumor properties in many different forms of cancer, including breast, prostate, gastric, hepatocarcinoma, glioblastoma, and pancreatic cancer. Potential of morusin as an alternative treatment method for resistant malignancies needs to be explored in animal models in order to move toward clinical trials. In the recent years several novel findings regarding the therapeutic potential of morusin have been made. This aim of this review is to provide an overview of the present understanding of morusin's beneficial effects on human health as well as provide a comprehensive and up-to-date discussion of morusin's anti-cancer properties with a special focus on in vitro and in vivo studies. This review will aid future research on the creation of polyphenolic medicines in the prenylflavone family, for the management and treatment of cancers.

Resting state functional connectivity demonstrates increased segregation in bilateral temporal lobe epilepsy

OBJECTIVE

Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. An increasingly identified subset of patients with TLE consists of those who show bilaterally independent temporal lobe seizures. The purpose of this study was to leverage network neuroscience to better understand the interictal whole brain network of bilateral TLE (BiTLE).

METHODS

In this study, using a multicenter resting state functional magnetic resonance imaging (rs-fMRI) data set, we constructed whole-brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting-state, whole-brain topological properties using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity. For each metric, we computed an average across all brain regions, and iterated this process across network densities. Curves of network density vs each network metric were compared between groups. Finally, we derived a combined metric, which we term the "integration-segregation axis," by combining whole-brain average clustering coefficient and global efficiency curves, and applying principal component analysis (PCA)-based dimensionality reduction.

RESULTS

Compared to UTLE, BiTLE had decreased global efficiency (p = .031), and decreased whole brain average participation coefficient across a range of network densities (p = .019). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p = .020). Differences in network properties separate BiTLE and UTLE along the integration-segregation axis, with regions within the axis having a specificity of up to 0.87 for BiTLE. Along the integration-segregation axis, UTLE patients with poor surgical outcomes were distributed in the same regions as BiTLE, and network metrics confirmed similar patterns of increased segregation in both BiTLE and poor outcome UTLE.

SIGNIFICANCE

Increased interictal whole-brain network segregation, as measured by rs-fMRI, is specific to BiTLE, as well as poor surgical outcome UTLE, and may assist in non-invasively identifying this patient population prior to intracranial electroencephalography or device implantation.

Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy

Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become indispensable tools in our armamentarium to localize epileptic seizures. New signal analysis techniques and the prospects of artificial intelligence and big data will offer unprecedented opportunities to further advance the field in the near future, ultimately resulting in improved quality of life for many patients with drug-resistant epilepsy. This article summarizes selected presentations from Day 1 of the two-day symposium "Neurophysiology, Neuropsychology, Epilepsy, 2022: Hills We Have Climbed and the Hills Ahead". Day 1 was dedicated to highlighting and honoring the work of Dr. Jean Gotman, a pioneer in EEG, intracranial EEG, simultaneous EEG/ functional magnetic resonance imaging, and signal analysis of epilepsy. The program focused on two main research directions of Dr. Gotman, and was dedicated to "High-frequency oscillations, a new biomarker of epilepsy" and "Probing the epileptic focus from inside and outside". All talks were presented by colleagues and former trainees of Dr. Gotman. The extended summaries provide an overview of historical and current work in the neurophysiology of epilepsy with emphasis on novel EEG biomarkers of epilepsy and source imaging and concluded with an outlook on the future of epilepsy research, and what is needed to bring the field to the next level.