Brain network excitatory/inhibitory imbalance is a biomarker for drug-naive Rolandic epilepsy: A radiomics strategy

Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network, and each type of epilepsy involves different brain-network changes that are implicated differently in the control and propagation of interictal or ictal discharges. Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice. An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tractography, diffusion kurtosis imaging-based fiber tractography, fiber ball imaging-based tractography, electroencephalography, functional magnetic resonance imaging, magnetoencephalography, positron emission tomography, molecular imaging, and functional ultrasound imaging have been extensively used to delineate epileptic networks. In this review, we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy, and extensively analyze the imaging mechanisms, advantages, limitations, and clinical application ranges of each technique. A greater focus on emerging advanced technologies, new data analysis software, a combination of multiple techniques, and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Exploration of interictal to ictal transition in epileptic seizures using a neural mass model

An epileptic seizure can usually be divided into three stages: interictal, preictal, and ictal. However, the seizure underlying the transition from interictal to ictal activities in the brain involves complex interactions between inhibition and excitation in groups of neurons. To explore this mechanism at the level of a single population, this paper employed a neural mass model, named the complete physiology-based model (cPBM), to reconstruct electroencephalographic (EEG) signals and to infer the changes in excitatory/inhibitory connections related to excitation-inhibition (E-I) balance based on an open dataset recorded for ten epileptic patients. Since epileptic signals display spectral characteristics, spectral dynamic causal modelling (DCM) was applied to quantify these frequency characteristics by maximizing the free energy in the framework of power spectral density (PSD) and estimating the cPBM parameters. In addition, to address the local maximum problem that DCM may suffer from, a hybrid deterministic DCM (H-DCM) approach was proposed, with a deterministic annealing-based scheme applied in two directions. The H-DCM approach adjusts the temperature introduced in the objective function by gradually decreasing the temperature to obtain relatively good initialization and then gradually increasing the temperature to search for a better estimation after each maximization. The results showed that (i) reconstructed EEG signals belonging to the three stages together with their PSDs can be reproduced from the estimated parameters of the cPBM; (ii) compared to DCM, traditional D-DCM and anti D-DCM, the proposed H-DCM shows higher free energies and lower root mean square error (RMSE), and it provides the best performance for all stages (e.g., the RMSEs between the reconstructed PSD computed from the reconstructed EEG signal and the sample PSD obtained from the real EEG signal are 0.33 ± 0.08, 0.67 ± 0.37 and 0.78 ± 0.57 in the interictal, preictal and ictal stages, respectively); and (iii) the transition from interictal to ictal activity can be explained by an increase in the connections between pyramidal cells and excitatory interneurons and between pyramidal cells and fast inhibitory interneurons, as well as a decrease in the self-loop connection of the fast inhibitory interneurons in the cPBM. Moreover, the E-I balance, defined as the ratio between the excitatory connection from pyramidal cells to fast inhibitory interneurons and the inhibitory connection with the self-loop of fast inhibitory interneurons, is also significantly increased during the epileptic seizure transition.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-09976-6.

Artificial intelligence in epilepsy - applications and pathways to the clinic

Artificial intelligence (AI) is rapidly transforming health care, and its applications in epilepsy have increased exponentially over the past decade. Integration of AI into epilepsy management promises to revolutionize the diagnosis and treatment of this complex disorder. However, translation of AI into neurology clinical practice has not yet been successful, emphasizing the need to consider progress to date and assess challenges and limitations of AI. In this Review, we provide an overview of AI applications that have been developed in epilepsy using a variety of data modalities: neuroimaging, electroencephalography, electronic health records, medical devices and multimodal data integration. For each, we consider potential applications, including seizure detection and prediction, seizure lateralization, localization of the seizure-onset zone and assessment for surgical or neurostimulation interventions, and review the performance of AI tools developed to date. We also discuss methodological considerations and challenges that must be addressed to successfully integrate AI into clinical practice. Our goal is to provide an overview of the current state of the field and provide guidance for leveraging AI in future to improve management of epilepsy.

The changing of α5-GABAA receptors expression and distribution participate in sevoflurane-induced learning and memory impairment in young mice

BACKGROUND

Sevoflurane is a superior agent for maintaining anesthesia during surgical procedures. However, the neurotoxic mechanisms of clinical concentration remain poorly understood. Sevoflurane can interfere with the normal function of neurons and synapses and impair cognitive function by acting on α5-GABAAR.

METHODS

Using MWM test, we evaluated cognitive abilities in mice following 1 h of anesthesia with 2.7%-3% sevoflurane. Based on hippocampal transcriptome analysis, we analyzed the differential genes and IL-6 24 h post-anesthesia. Western blot and RT-PCR were performed to measure the levels of α5-GABAAR, Radixin, P-ERM, P-Radixin, Gephyrin, IL-6, and ROCK. The spatial distribution and expression of α5-GABAAR on neuronal somata were analyzed using histological and three-dimensional imaging techniques.

RESULTS

MWM test indicated that partial long-term learning and memory impairment. Combining molecular biology and histological analysis, our studies have demonstrated that sevoflurane induces immunosuppression, characterized by reduced IL-6 expression levels, and that enhanced Radixin dephosphorylation undermines the microstructural stability of α5-GABAAR, leading to its dissociation from synaptic exterior and resulting in a disordered distribution in α5-GABAAR expression within neuronal cell bodies. On the synaptic cleft, the expression level of α5-GABAAR remained unchanged, the spatial distribution became more compact, with an increased fluorescence intensity per voxel. On the extra-synaptic space, the expression level of α5-GABAAR decreased within unchanged spatial distribution, accompanied by an increased fluorescence intensity per voxel.

CONCLUSION

Dysregulated α5-GABAAR expression and distribution contributes to sevoflurane-induced partial long-term learning and memory impairment, which lays the foundation for elucidating the underlying mechanisms in future studies.

The effect of ferroptosis-related mitochondrial dysfunction in the development of temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common form of epileptic syndrome. It has been established that due to its complex pathogenesis, a considerable proportion of TLE patients often progress to drug-resistant epilepsy. Ferroptosis has emerged as an important neuronal death mechanism in TLE, which is primarily influenced by lipid accumulation and oxidative stress. In previous studies of ferroptosis, more attention has been focused on the impact of changes in the levels of proteins related to the redox equilibrium and signaling pathways on epileptic seizures. However, it is worth noting that the oxidative-reduction changes in different organelles may have different pathophysiological significance in the process of ferroptosis-related diseases. Mitochondria, as a key organelle involved in ferroptosis, its structural damage and functional impairment can lead to energy metabolism disorders and disruption of the excitatory inhibitory balance, significantly increasing the susceptibility to epileptic seizures. Therefore, secondary mitochondrial dysfunction in the process of ferroptosis could play a crucial role in TLE pathogenesis. This review focuses on ferroptosis and mitochondria, discussing the pathogenic role of ferroptosis-related mitochondrial dysfunction in TLE, thus aiming to provide novel insights and potential implications of ferroptosis-related secondary mitochondrial dysfunction in epileptic seizures and to offer new insights for the precise exploration of ferroptosis-related therapeutic targets for TLE patients.

Knockdown of SIK3 in the CA1 Region can Reduce Seizure Susceptibility in Mice by Inhibiting Decreases in GABAAR α1 Expression

Imbalance between excitation and inhibition is an important cause of epilepsy. Salt-inducible kinase 1 (SIK1) gene mutation can cause epilepsy. In this study, we first found that the expression of SIK3 is increased after epilepsy. Furthermore, the role of SIK3 in epilepsy was explored. In cultured hippocampal neurons, we used Pterosin B, a selective SIK3 inhibitor that can inhibit epileptiform discharges induced by the convulsant drug cyclothiazide (a positive allosteric modulator of AMPA receptors, CTZ). Knockdown of SIK3 inhibited epileptiform discharges and increased the amplitude of miniature inhibitory postsynaptic currents (mIPSCs). In mice, knockdown of SIK3 reduced epilepsy susceptibility in a pentylenetetrazole (a GABAA receptor antagonist, PTZ) acute kindling experiment and increased the expression of GABAA receptor α1. In conclusion, our results suggest that blockade or knockdown of SIK3 can inhibit epileptiform discharges and that SIK3 has the potential to be a novel target for epilepsy treatment.

Causal Connectivity Network Analysis of Ictal Electrocorticogram With Temporal Lobe Epilepsy Based on Dynamic Phase Transfer Entropy

Temporallobe epilepsy (TLE) has been conceptualized as a brain network disease, which generates brain connectivity dynamics within and beyond the temporal lobe structures in seizures. The hippocampus is a representative epileptogenic focus in TLE. Understanding the causal connectivity in terms of brain network during seizures is crucial in revealing the triggering mechanism of epileptic seizures originating from the hippocampus (HPC) spread to the lateral temporal cortex (LTC) by ictal electrocorticogram (ECoG), particularly in high-frequency oscillations (HFOs) bands. In this study, we proposed the unified-epoch dynamic causality analysis method to investigate the causal influence dynamics between two brain regions (HPC and LTC) at interictal and ictal phases in the frequency range of 1-500 Hz by introducing the phase transfer entropy (PTE) out/in-ratio and sliding window. We also proposed PTE-based machine learning algorithms to identify epileptogenic zone (EZ). Nine patients with a total of 26 seizures were included in this study. We hypothesized that: 1) HPC is the focus with the stronger causal connectivity than that in LTC in the ictal state at gamma and HFOs bands. 2) Causal connectivity in the ictal phase shows significant changes compared to that in the interictal phase. 3) The PTE out/in-ratio in the HFOs band can identify the EZ with the best prediction performance.

Atypical functional connectivity hierarchy in Rolandic epilepsy

Functional connectivity hierarchy is an important principle in the process of brain functional organization and an important feature reflecting brain development. However, atypical brain network hierarchy organization in Rolandic epilepsy have not been systematically investigated. We examined connectivity alteration with age and its relation to epileptic incidence, cognition, or underlying genetic factors in 162 cases of Rolandic epilepsy and 117 typically developing children, by measuring fMRI multi-axis functional connectivity gradients. Rolandic epilepsy is characterized by contracting and slowing expansion of the functional connectivity gradients, highlighting the atypical age-related change of the connectivity hierarchy in segregation properties. The gradient alterations are relevant to seizure incidence, cognition, and connectivity deficit, and development-associated genetic basis. Collectively, our approach provides converging evidence for atypical connectivity hierarchy as a system-level substrate of Rolandic epilepsy, suggesting this is a disorder of information processing across multiple functional domains, and established a framework for large-scale brain hierarchical research.

Effects of altered excitation-inhibition imbalance by repetitive transcranial magnetic stimulation for self-limited epilepsy with centrotemporal spikes

Objectives

Patients with self-limited epilepsy with centrotemporal spikes (SeLECTS) with electrical status epilepticus in sleep (ESES) have generalized cognitive impairment, yet treatment options are limited. Our study aimed to examine the therapeutic effects of repetitive transcranial magnetic stimulation (rTMS) on SeLECTS with ESES. In addition, we applied electroencephalography (EEG) aperiodic components (offset and slope) to investigate the improvement of rTMS on the excitation-inhibition imbalance (E-I imbalance) in the brain of this group of children.

Methods

Eight SeLECTS patients with ESES were included in this study. Low-frequency rTMS (≤1 Hz) was applied for 10 weekdays in each patient. To assess the clinical efficacy and changes in E-I imbalance, EEG recordings were performed both before and after rTMS. Seizure-reduction rate and spike-wave index (SWI) were measured to investigate the clinical effects of rTMS. The aperiodic offset and slope were calculated to explore the effect of rTMS on E-I imbalance.

Results

Five of the eight patients (62.5%) were seizure-free within 3 months after stimulation, with treatment effects decreasing with longer follow-ups. The SWI decreased significantly at 3 and 6 months after rTMS compared with the baseline (P = 0.0157 and P = 0.0060, respectively). The offset and slope were compared before rTMS and within 3 months after stimulation. The results showed a significant reduction in the offset after stimulation (P < 0.0001). There was a remarkable increase in slope after the stimulation (P < 0.0001).

Conclusion

Patients achieved favorable outcomes in the first 3 months after rTMS. The ameliorative effect of rTMS on SWI may last up to 6 months. Low-frequency rTMS could reduce firing rates in neuronal populations throughout the brain, which was most pronounced at the site of stimulation. A significant reduction in the slope after rTMS treatment suggested an improvement in the E-I imbalance in the SeLECTS.

Moving from phenomenological to predictive modelling: Progress and pitfalls of modelling brain stimulation in-silico

Brain stimulation is an increasingly popular neuromodulatory tool used in both clinical and research settings; however, the effects of brain stimulation, particularly those of non-invasive stimulation, are variable. This variability can be partially explained by an incomplete mechanistic understanding, coupled with a combinatorial explosion of possible stimulation parameters. Computational models constitute a useful tool to explore the vast sea of stimulation parameters and characterise their effects on brain activity. Yet the utility of modelling stimulation in-silico relies on its biophysical relevance, which needs to account for the dynamics of large and diverse neural populations and how underlying networks shape those collective dynamics. The large number of parameters to consider when constructing a model is no less than those needed to consider when planning empirical studies. This piece is centred on the application of phenomenological and biophysical models in non-invasive brain stimulation. We first introduce common forms of brain stimulation and computational models, and provide typical construction choices made when building phenomenological and biophysical models. Through the lens of four case studies, we provide an account of the questions these models can address, commonalities, and limitations across studies. We conclude by proposing future directions to fully realise the potential of computational models of brain stimulation for the design of personalized, efficient, and effective stimulation strategies.

Structural and functional changes in drug-naïve benign childhood epilepsy with centrotemporal spikes and their associated gene expression profiles

Benign epilepsy with centrotemporal spikes (BECTS) is a common pediatric epilepsy syndrome that has been widely reported to show abnormal brain structure and function. However, the genetic mechanisms underlying structural and functional changes remain largely unknown. Based on the structural and resting-state functional magnetic resonance imaging data of 22 drug-naïve children with BECTS and 33 healthy controls, we conducted voxel-based morphology (VBM) and fractional amplitude of low-frequency fluctuation (fALFF) analyses to compare cortical morphology and spontaneous brain activity between the 2 groups. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging spatial correlation analyses were applied to explore gene expression profiles associated with gray matter volume (GMV) and fALFF changes in BECTS. VBM analysis demonstrated significantly increased GMV in the right brainstem and right middle cingulate gyrus in BECTS. Moreover, children with BECTS exhibited significantly increased fALFF in left temporal pole, while decreased fALFF in right thalamus and left precuneus. These brain structural and functional alterations were closely related to behavioral and cognitive deficits, and the fALFF-linked gene expression profiles were enriched in voltage-gated ion channel and synaptic activity as well as neuron projection. Our findings suggest that brain morphological and functional abnormalities in children with BECTS involve complex polygenic genetic mechanisms.

Preoperative MRI for postoperative seizure prediction: a radiomics study of dysembryoplastic neuroepithelial tumor and a systematic review

OBJECTIVE

In this systematic review the authors aimed to evaluate the effectiveness and superiority of radiomics in detecting tiny epilepsy lesions and to conduct original research in the use of radiomics for preliminary prediction of postoperative seizures in patients with dysembryoplastic neuroepithelial tumor (DNET).

METHODS

The PubMed and Web of Science databases were searched from the earliest record, January 1, 2018, to December 29, 2021, for reports of the detection of epilepsy using radiomics, and the resulting articles were carefully checked according to the PRISMA 2020 guidelines. The authors then conducted original research by evaluating MR images in 18 patients, who were then separated into two groups, the epilepsy recurrence group (ERG) and the epilepsy nonrecurrence group. The tumor region and the edema region were segmented manually by 3D Slicer. The radiomics data were extracted from MR images by using “Slicer Radiomics” running on Mac OS X. Tumor regions were observed with T1-weighted imaging, and edema with FLAIR imaging. Radiomics features with significant differences were selected through comparison according to epilepsy relapses performed with the Mann-Whitney U-test. The edema and tumor regions were also compared within groups to identify their distinctive features. Radiomics features were tested to verify their ability to predict recurrence epilepsy by receiver operating characteristic curve.

RESULTS

This systematic review located 9 original articles related to epilepsy and radiomics published from 2018 to 2021. The reported studies demonstrated that radiomics is useful for detecting tiny epilepsy lesions. Among the radiomics features used, the predictive ability of the area under the curve was more than 0.8. The heterogeneity of the peritumoral edema region was found to be higher in the ERG.

CONCLUSIONS

Satellite lesions in the peritumoral edema region of DNET patients may cause epilepsy recurrence, and radiomics is an emerging method to detect and evaluate these epilepsy-associated lesions.

Computerized application for epilepsy in China: Does the era of artificial intelligence comes?

Epilepsy, one of the most common neurological diseases in China, is notorious for its spontaneous, unprovoked and recurrent seizures. The etiology of epilepsy varies among individual patients, including congenital gene mutation, traumatic injury, infections, etc. This heterogeneity partly hampered the accurate diagnosis and choice of appropriate treatments. Encouragingly, great achievements have been achieved in computational science, making it become a key player in medical fields gradually and bringing new hope for rapid and accurate diagnosis as well as targeted therapies in epilepsy. Here, we historically review the advances of computerized applications in epilepsy-especially those tremendous findings achieved in China-for different purposes including seizure prediction, localization of epileptogenic zone, post-surgical prognosis, etc. Special attentions are paid to the great progress based on artificial intelligence (AI), which is more "sensitive", "smart" and "in-depth" than human capacities. At last, we give a comprehensive discussion about the disadvantages and limitations of current computerized applications for epilepsy and propose some future directions as further stepping stones to embrace "the era of AI" in epilepsy.

Interictal epileptiform discharges changed epilepsy-related brain network architecture in BECTS

To investigate directed information flow of epileptiform activity in benign epilepsy with centrotemporal spikes (BECTS) during ictal epileptiform discharges (IEDs) and non-IEDs periods. In this multi-center study, a total of 188 subjects, including 50 BECTS and 138 normal children's controls (NCs) from three different centers (Center 1: females/males, 38/55; mean age, 9.33 ± 2.6 years; Center 2: females/males,7/10; mean age, 8.59 ± 2.32 years; Center 3: females/males, 14/14; mean age, 13 ± 3.42 years) were recruited. The BECTS were classified into IEDs (females/males, 12/15; mean age, 8.15 ± 1.68 years) and non-IEDs (females/males, 10/13; mean age, 9.09 ± 1.98 years) subgroups depending on presence of central-temporal spikes from an EEG-fMRI examination. Three new methods, structural equation parametric modeling, dynamic causal modeling and granger causality density (GCD) were used to determine optimal network architectures for BECTS. Three multicentric NCs determined a reliable and consistent network architecture by structural equation parametric modeling method. Further analyses were used for IEDs and non-IEDs to determine the brain network architecture by structural equation parametric modeling, dynamic causal modeling and GCD, respectively. The brain network architecture of IEDs substate, non-IEDs substate and NCs are different. IEDs promoted the driving effect of the Rolandic areas with more output information flows, and increased the targeted effect of the top of pre-/post-central gyrus with more input information flows. The information flow arises from the Rolandic areas, and subsequently propagates to the top of pre-/post-central gyrus and thalamus. From non-IEDs status to IEDs status, the thalamus load may play an important role in the modulation and regulation of epileptiform activity. These findings shed new light on pathophysiological mechanism of directed localization of epileptiform activity in BECTS.