Super-Resolution Imaging of the Extracellular Space in Living Brain Tissue.

The extracellular space (ECS) of the brain has an extremely complex spatial organization, which has defied conventional light microscopy. Consequently, despite a marked interest in the physiological roles of brain ECS, its structure and dynamics remain largely inaccessible for experimenters. We combined 3D-STED microscopy and fluorescent labeling of the extracellular fluid to develop super-resolution shadow imaging (SUSHI) of brain ECS in living organotypic brain slices. SUSHI enables quantitative analysis of ECS structure and reveals dynamics on multiple scales in response to a variety of physiological stimuli. Because SUSHI produces sharp negative images of all cellular structures, it also enables unbiased imaging of unlabeled brain cells with respect to their anatomical context. Moreover, the extracellular labeling strategy greatly alleviates problems of photobleaching and phototoxicity associated with traditional imaging approaches. As a straightforward variant of STED microscopy, SUSHI provides unprecedented access to the structure and dynamics of live brain ECS and neuropil.

Control of neuronal excitation-inhibition balance by BMP-SMAD1 signalling.

Throughout life, neuronal networks in the mammalian neocortex maintain a balance of excitation and inhibition, which is essential for neuronal computation1,2. Deviations from a balanced state have been linked to neurodevelopmental disorders, and severe disruptions result in epilepsy3-5. To maintain balance, neuronal microcircuits composed of excitatory and inhibitory neurons sense alterations in neural activity and adjust neuronal connectivity and function. Here we identify a signalling pathway in the adult mouse neocortex that is activated in response to increased neuronal network activity. Overactivation of excitatory neurons is signalled to the network through an increase in the levels of BMP2, a growth factor that is well known for its role as a morphogen in embryonic development. BMP2 acts on parvalbumin-expressing (PV) interneurons through the transcription factor SMAD1, which controls an array of glutamatergic synapse proteins and components of perineuronal nets. PV-interneuron-specific disruption of BMP2-SMAD1 signalling is accompanied by a loss of glutamatergic innervation in PV cells, underdeveloped perineuronal nets and decreased excitability. Ultimately, this impairment of the functional recruitment of PV interneurons disrupts the cortical excitation-inhibition balance, with mice exhibiting spontaneous epileptic seizures. Our findings suggest that developmental morphogen signalling is repurposed to stabilize cortical networks in the adult mammalian brain.

Structural and functional reorganization of inhibitory synapses by activity-dependent cleavage of neuroligin-2.

Recent evidence has demonstrated that the transsynaptic nanoscale organization of synaptic proteins plays a crucial role in regulating synaptic strength in excitatory synapses. However, the molecular mechanism underlying this transsynaptic nanostructure in inhibitory synapses still remains unclear and its impact on synapse function in physiological or pathological contexts has not been demonstrated. In this study, we utilized an engineered proteolysis technique to investigate the effects of acute cleavage of neuroligin-2 (NL2) on synaptic transmission. Our results show that the rapid cleavage of NL2 led to impaired synaptic transmission by reducing both neurotransmitter release probability and quantum size. These changes were attributed to the dispersion of RIM1/2 and GABAA receptors and a weakened spatial alignment between them at the subsynaptic scale, as observed through superresolution imaging and model simulations. Importantly, we found that endogenous NL2 undergoes rapid MMP9-dependent cleavage during epileptic activities, which further exacerbates the decrease in inhibitory transmission. Overall, our study demonstrates the significant impact of nanoscale structural reorganization on inhibitory transmission and unveils ongoing modulation of mature GABAergic synapses through active cleavage of NL2 in response to hyperactivity.

Hemispheric functional organization, as revealed by naturalistic neuroimaging, in pediatric epilepsy patients with cortical resections.

Functional changes in the pediatric brain following neural injuries attest to remarkable feats of plasticity. Investigations of the neurobiological mechanisms that underlie this plasticity have largely focused on activation in the penumbra of the lesion or in contralesional, homotopic regions. Here, we adopt a whole-brain approach to evaluate the plasticity of the cortex in patients with large unilateral cortical resections due to drug-resistant childhood epilepsy. We compared the functional connectivity (FC) in patients' preserved hemisphere with the corresponding hemisphere of matched controls as they viewed and listened to a movie excerpt in a functional magnetic resonance imaging (fMRI) scanner. The preserved hemisphere was segmented into 180 and 200 parcels using two different anatomical atlases. We calculated all pairwise multivariate statistical dependencies between parcels, or parcel edges, and between 22 and 7 larger-scale functional networks, or network edges, aggregated from the smaller parcel edges. Both the left and right hemisphere-preserved patient groups had widespread reductions in FC relative to matched controls, particularly for within-network edges. A case series analysis further uncovered subclusters of patients with distinctive edgewise changes relative to controls, illustrating individual postoperative connectivity profiles. The large-scale differences in networks of the preserved hemisphere potentially reflect plasticity in the service of maintained and/or retained cognitive function.

Catastrophic Epilepsies of Childhood.

The tragedy of epilepsy emerges from the combination of its high prevalence, impact upon sufferers and their families, and unpredictability. Childhood epilepsies are frequently severe, presenting in infancy with pharmaco-resistant seizures; are often accompanied by debilitating neuropsychiatric and systemic comorbidities; and carry a grave risk of mortality. Here, we review the most current basic science and translational research findings on several of the most catastrophic forms of pediatric epilepsy. We focus largely on genetic epilepsies and the research that is discovering the mechanisms linking disease genes to epilepsy syndromes. We also describe the strides made toward developing novel pharmacological and interventional treatment strategies to treat these disorders. The research reviewed provides hope for a complete understanding of, and eventual cure for, these childhood epilepsy syndromes.

Parameter estimation in a whole-brain network model of epilepsy: Comparison of parallel global optimization solvers.

The Virtual Epileptic Patient (VEP) refers to a computer-based representation of a patient with epilepsy that combines personalized anatomical data with dynamical models of abnormal brain activities. It is capable of generating spatio-temporal seizure patterns that resemble those recorded with invasive methods such as stereoelectro EEG data, allowing for the evaluation of clinical hypotheses before planning surgery. This study highlights the effectiveness of calibrating VEP models using a global optimization approach. The approach utilizes SaCeSS, a cooperative metaheuristic algorithm capable of parallel computation, to yield high-quality solutions without requiring excessive computational time. Through extensive benchmarking on synthetic data, our proposal successfully solved a set of different configurations of VEP models, demonstrating better scalability and superior performance against other parallel solvers. These results were further enhanced using a Bayesian optimization framework for hyperparameter tuning, with significant gains in terms of both accuracy and computational cost. Additionally, we added a scalable uncertainty quantification phase after model calibration, and used it to assess the variability in estimated parameters across different problems. Overall, this study has the potential to improve the estimation of pathological brain areas in drug-resistant epilepsy, thereby to inform the clinical decision-making process.

Method for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activity.

Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals. Trial Registration: NCT03946618.

Thalamic epileptic spikes disrupt sleep spindles in patients with epileptic encephalopathy.

In severe epileptic encephalopathies, epileptic activity contributes to progressive cognitive dysfunction. Epileptic encephalopathies share the trait of spike-wave activation during non-REM sleep (EE-SWAS), a sleep stage dominated by sleep spindles, which are brain oscillations known to coordinate offline memory consolidation. Epileptic activity has been proposed to hijack the circuits driving these thalamocortical oscillations, thereby contributing to cognitive impairment. Using a unique dataset of simultaneous human thalamic and cortical recordings in subjects with and without EE-SWAS, we provide evidence for epileptic spike interference of thalamic sleep spindle production in patients with EE-SWAS. First, we show that epileptic spikes and sleep spindles are both predicted by slow oscillations during stage two sleep (N2), but at different phases of the slow oscillation. Next, we demonstrate that sleep-activated cortical epileptic spikes propagate to the thalamus (thalamic spike rate increases after a cortical spike, P ≈ 0). We then show that epileptic spikes in the thalamus increase the thalamic spindle refractory period (P ≈ 0). Finally, we show that in three patients with EE-SWAS, there is a downregulation of sleep spindles for 30 s after each thalamic spike (P < 0.01). These direct human thalamocortical observations support a proposed mechanism for epileptiform activity to impact cognitive function, wherein epileptic spikes inhibit thalamic sleep spindles in epileptic encephalopathy with spike and wave activation during sleep.

Spike ripples localize the epileptogenic zone best: an international intracranial study.

We evaluated whether spike ripples, the combination of epileptiform spikes and ripples, provide a reliable and improved biomarker for the epileptogenic zone compared with other leading interictal biomarkers in a multicentre, international study. We first validated an automated spike ripple detector on intracranial EEG recordings. We then applied this detector to subjects from four centres who subsequently underwent surgical resection with known 1-year outcomes. We evaluated the spike ripple rate in subjects cured after resection [International League Against Epilepsy Class 1 outcome (ILAE 1)] and those with persistent seizures (ILAE 2-6) across sites and recording types. We also evaluated available interictal biomarkers: spike, spike-gamma, wideband high frequency oscillation (HFO, 80-500 Hz), ripple (80-250 Hz) and fast ripple (250-500 Hz) rates using previously validated automated detectors. The proportion of resected events was computed and compared across subject outcomes and biomarkers. Overall, 109 subjects were included. Most spike ripples were removed in subjects with ILAE 1 outcome (P < 0.001), and this was qualitatively observed across all sites and for depth and subdural electrodes (P < 0.001 and P < 0.001, respectively). Among ILAE 1 subjects, the mean spike ripple rate was higher in the resected volume (0.66/min) than in the non-removed tissue (0.08/min, P < 0.001). A higher proportion of spike ripples were removed in subjects with ILAE 1 outcomes compared with ILAE 2-6 outcomes (P = 0.06). Among ILAE 1 subjects, the proportion of spike ripples removed was higher than the proportion of spikes (P < 0.001), spike-gamma (P < 0.001), wideband HFOs (P < 0.001), ripples (P = 0.009) and fast ripples (P = 0.009) removed. At the individual level, more subjects with ILAE 1 outcomes had the majority of spike ripples removed (79%, 38/48) than spikes (69%, P = 0.12), spike-gamma (69%, P = 0.12), wideband HFOs (63%, P = 0.03), ripples (45%, P = 0.01) or fast ripples (36%, P < 0.001) removed. Thus, in this large, multicentre cohort, when surgical resection was successful, the majority of spike ripples were removed. Furthermore, automatically detected spike ripples localize the epileptogenic tissue better than spikes, spike-gamma, wideband HFOs, ripples and fast ripples.

Aperiodic components of local field potentials reflect inherent differences between cortical and subcortical activity.

Information flow in brain networks is reflected in local field potentials that have both periodic and aperiodic components. The 1/fχ aperiodic component of the power spectra tracks arousal and correlates with other physiological and pathophysiological states. Here we explored the aperiodic activity in the human thalamus and basal ganglia in relation to simultaneously recorded cortical activity. We elaborated on the parameterization of the aperiodic component implemented by specparam (formerly known as FOOOF) to avoid parameter unidentifiability and to obtain independent and more easily interpretable parameters. This allowed us to seamlessly fit spectra with and without an aperiodic knee, a parameter that captures a change in the slope of the aperiodic component. We found that the cortical aperiodic exponent χ, which reflects the decay of the aperiodic component with frequency, is correlated with Parkinson's disease symptom severity. Interestingly, no aperiodic knee was detected from the thalamus, the pallidum, or the subthalamic nucleus, which exhibited an aperiodic exponent significantly lower than in cortex. These differences were replicated in epilepsy patients undergoing intracranial monitoring that included thalamic recordings. The consistently lower aperiodic exponent and lack of an aperiodic knee from all subcortical recordings may reflect cytoarchitectonic and/or functional differences. SIGNIFICANCE STATEMENT: The aperiodic component of local field potentials can be modeled to produce useful and reproducible indices of neural activity. Here we refined a widely used phenomenological model for extracting aperiodic parameters (namely the exponent, offset and knee), with which we fit cortical, basal ganglia, and thalamic intracranial local field potentials, recorded from unique cohorts of movement disorders and epilepsy patients. We found that the aperiodic exponent in motor cortex is higher in Parkinson's disease patients with more severe motor symptoms, suggesting that aperiodic features may have potential as electrophysiological biomarkers for movement disorders symptoms. Remarkably, we found conspicuous differences in the aperiodic parameters of basal ganglia and thalamic signals compared to those from neocortex.

Subject-based assessment of large-scale integration dynamics in epileptic brain networks: insights from the intrinsic ignition framework.

This study examined the dynamic properties of brain regions involved in the genesis and spread of seizures in 10 individuals diagnosed with pharmacoresistant focal epilepsy. The patients and 30 healthy controls underwent resting-state functional magnetic resonance imaging scans and the brain's functional network dynamics were analyzed using the intrinsic ignition framework. Comparative statistical analyses examined the differences in the integration and metastability measures in both groups in the whole brain and specific local brain regions. Invasive electroencephalography evaluations validated the findings of significant global and regional changes in the patient's brain network dynamics. There was a marked increase in global integration and metastability across the brain, reflecting substantial alterations in the overall connectivity and flexibility of the functional networks. Specific brain regions exhibited paradoxical dynamics within the seizure onset zone, with decreased intrinsic ignition and increased metastability. Increased intrinsic ignition was observed in remote brain regions, suggesting a reorganization of the brain network hubs and potential pathways for seizure propagation. Using the intrinsic ignition framework provided insights into dynamic alterations in the brain networks of patients with epilepsy. These have increased our understanding of the mechanisms underlying epileptic seizures and may guide the development of diagnostic biomarkers and targeted therapeutic interventions.

Seizure-induced strengthening of a recurrent excitatory circuit in the dentate gyrus is proconvulsant.

Epilepsy is a devastating brain disorder for which effective treatments are very limited. There is growing interest in early intervention, which requires a better mechanistic understanding of the early stages of this disorder. While diverse brain insults can lead to epileptic activity, a common cellular mechanism relies on uncontrolled recurrent excitatory activity. In the dentate gyrus, excitatory mossy cells (MCs) project extensively onto granule cells (GCs) throughout the hippocampus, thus establishing a recurrent MC-GC-MC excitatory loop. MCs are implicated in temporal lobe epilepsy, a common form of epilepsy, but their role during initial seizures (i.e., before the characteristic MC loss that occurs in late stages) is unclear. Here, we show that initial seizures acutely induced with an intraperitoneal kainic acid (KA) injection in adult mice, a well-established model that leads to experimental epilepsy, not only increased MC and GC activity in vivo but also triggered a brain-derived neurotrophic factor (BDNF)-dependent long-term potentiation (LTP) at MC-GC excitatory synapses. Moreover, in vivo induction of MC-GC LTP using MC-selective optogenetic stimulation worsened KA-induced seizures. Conversely, Bdnf genetic removal from GCs, which abolishes LTP, and selective MC silencing were both anticonvulsant. Thus, initial seizures are associated with MC-GC synaptic strengthening, which may promote later epileptic activity. Our findings reveal a potential mechanism of epileptogenesis that may help in developing therapeutic strategies for early intervention.

Astrocyte switch to the hyperactive mode.

Increasing pieces of evidence have suggested that astrocyte function has a strong influence on neuronal activity and plasticity, both in physiological and pathophysiological situations. In epilepsy, astrocytes have been shown to respond to epileptic neuronal seizures; however, whether they can act as a trigger for seizures has not been determined. Here, using the copper implantation method, spontaneous neuronal hyperactivity episodes were reliably induced during the week following implantation. With near 24-h continuous recording for over 1 week of the local field potential with in vivo electrophysiology and astrocyte cytosolic Ca2+ with the fiber photometry method, spontaneous occurrences of seizure episodes were captured. Approximately 1 day after the implantation, isolated aberrant astrocyte Ca2+ events were often observed before they were accompanied by neuronal hyperactivity, suggesting the role of astrocytes in epileptogenesis. Within a single developed episode, astrocyte Ca2+ increase preceded the neuronal hyperactivity by ~20 s, suggesting that actions originating from astrocytes could be the trigger for the occurrence of epileptic seizures. Astrocyte-specific stimulation by channelrhodopsin-2 or deep-brain direct current stimulation was capable of inducing neuronal hyperactivity. Injection of an astrocyte-specific metabolic inhibitor, fluorocitrate, was able to significantly reduce the magnitude of spontaneously occurring neuronal hyperactivity. These results suggest that astrocytes have a role in triggering individual seizures and the reciprocal astrocyte-neuron interactions likely amplify and exacerbate seizures. Therefore, future epilepsy treatment could be targeted at astrocytes to achieve epilepsy control.

Nonlinear analysis and recognition of epileptic EEG signals in different stages.

It is a hot problem in epilepsy research to detect and predict seizures by EEG signals. Clinically, it is generally observed that there are only sudden abnormal signals during the ictal stage, but there is no significant difference in the EEG signal between the interictal and preictal stages. To solve the problem that preictal signals are difficult to recognize clinically, and then effectively improve the recognition efficiency of epileptic seizures, so, in this paper, some nonlinear methods are comprehensively used to extract the hidden information in the EEG signals in different stages, namely, phase space reconstruction (PSR), Poincaré section (PS), synchroextracting transform (SET), and machine learning for EEG signal analysis. First, PSR based on C-C method is used, and the results show that there are different diffuse attractor trajectories of the signals in different stages. Second, the confidence ellipse (CE) is constructed by using the scatter diagram of the corresponding trajectory on PS, and the aspect ratio and area of the ellipse are calculated. The results show that there is an interesting transitional phenomenon in preictal stage. To recognize ictal and preictal signals, time-frequency (TF) spectrums, which are processed by SET, are fed into the convolutional neural network (CNN) classifier. The accuracy of recognizing ictal and preictal signals reaches 99.7% and 93.7%, respectively. To summarize, our results based on nonlinear method provide new research ideas for seizure detection and prediction.NEW & NOTEWORTHY Our results based on nonlinear method have better practical significance and clinical application value and improved the prediction efficiency of epileptic EEG signals effectively. This work provides direct insight into the application of these biomarkers for seizure detection and prediction.

Increased excitatory connectivity and epileptiform activity in thrombospondin1/2 knockout mice following cortical trauma.

Thrombospondins (TSPs) are astrocyte-secreted extracellular matrix proteins that play key roles as regulators of synaptogenesis in the central nervous system. We previously showed that TSP1/2 are upregulated in the partial neocortical isolation model ("undercut" or "UC" below) of posttraumatic epileptogenesis and may contribute to abnormal axonal sprouting, aberrant synaptogenesis and epileptiform discharges in the UC cortex. These results led to the hypothesis that posttraumatic epileptogeneis would be reduced in TSP1/2 knockout (TSP1/2 KO) mice. To test the hypothesis, we made UC lesions at P21, and subsequent experiments were conducted 14d later at P35. Ex vivo extracellular single or multi-electrode field potential recordings were obtained from layer V in cortical slices at P35 and in vivo video-EEGs of spontaneous epileptiform bursts were recorded to examine the effect of TSP1/2 deletion on epileptogenesis following cortical injury. Immunohistochemical experiments were performed to assess the effect of TSP1/2 KO + UC on the number of putative excitatory synapses and the expression of TSP4 and HEVIN, other astrocytic proteins known to up-regulate excitatory synapse formation. Unexpectedly, our results showed that, compared with WT + UC mice, TSP1/2 KO + UC mice displayed increased epileptiform activity, as indicated by 1) increased incidence and more rapid propagation of evoked and spontaneous epileptiform discharges in UC neocortical slices; 2) increased occurrence of spontaneous epileptiform discharges in vivo. There was an associated increase in the density of VLUT1/PSD95-IR colocalizations (putative excitatory synapses) and significantly upregulated TSP4- and HEVIN-IR in TSP1/2 KO + UC versus WT + UC mice. Results suggest that TSP1/2 deletion plays a potential epileptogenic role following neocortical injury, associated with compensatory upregulation of TSP4 and HEVIN, which may contribute to the increase in the density of excitatory synapses and resulting neural network hyperexcitability.

EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy.

Electro/Magneto-EncephaloGraphy (EEG/MEG) source imaging (EMSI) of epileptic activity from deep generators is often challenging due to the higher sensitivity of EEG/MEG to superficial regions and to the spatial configuration of subcortical structures. We previously demonstrated the ability of the coherent Maximum Entropy on the Mean (cMEM) method to accurately localize the superficial cortical generators and their spatial extent. Here, we propose a depth-weighted adaptation of cMEM to localize deep generators more accurately. These methods were evaluated using realistic MEG/high-density EEG (HD-EEG) simulations of epileptic activity and actual MEG/HD-EEG recordings from patients with focal epilepsy. We incorporated depth-weighting within the MEM framework to compensate for its preference for superficial generators. We also included a mesh of both hippocampi, as an additional deep structure in the source model. We generated 5400 realistic simulations of interictal epileptic discharges for MEG and HD-EEG involving a wide range of spatial extents and signal-to-noise ratio (SNR) levels, before investigating EMSI on clinical HD-EEG in 16 patients and MEG in 14 patients. Clinical interictal epileptic discharges were marked by visual inspection. We applied three EMSI methods: cMEM, depth-weighted cMEM and depth-weighted minimum norm estimate (MNE). The ground truth was defined as the true simulated generator or as a drawn region based on clinical information available for patients. For deep sources, depth-weighted cMEM improved the localization when compared to cMEM and depth-weighted MNE, whereas depth-weighted cMEM did not deteriorate localization accuracy for superficial regions. For patients' data, we observed improvement in localization for deep sources, especially for the patients with mesial temporal epilepsy, for which cMEM failed to reconstruct the initial generator in the hippocampus. Depth weighting was more crucial for MEG (gradiometers) than for HD-EEG. Similar findings were found when considering depth weighting for the wavelet extension of MEM. In conclusion, depth-weighted cMEM improved the localization of deep sources without or with minimal deterioration of the localization of the superficial sources. This was demonstrated using extensive simulations with MEG and HD-EEG and clinical MEG and HD-EEG for epilepsy patients.

Contribution of prefrontal cortex and ventral hippocampus to anxiety in young epileptic mice.

Children with epilepsy are particularly vulnerable to anxiety disorders, where these disorders are frequently underdiagnosed and untreated. Despite the high prevalence of anxiety in epilepsy, the underlying neurobiological mechanisms are not fully understood. The medial prefrontal cortex (mPFC) and ventral hippocampus (vHPC) are key brain regions implicated in the genesis and modulation of anxiety, and their interactions play a crucial role in emotional processing including anxiety. We utilized a pilocarpine-induced epilepsy model in young mice (7 weeks old) to assess anxiety-like behaviors using the open field test (OFT), light/dark box, and elevated plus maze (EPM). Local field potential (LFP) recordings were conducted to examine theta power and coherence between the mPFC and vHPC. LFP recordings revealed significantly altered theta power variation in both the mPFC and vHPC during exposure to anxiogenic contexts, suggesting the involvement of these regions in anxiety in the young epileptic mice. Notably, theta-frequency synchrony between the mPFC and vHPC was not significantly altered in the young epileptic mice, indicating that altered theta power rather than inter-regional synchrony may underlie anxiety behaviors in young epileptic mice. Furthermore, we demonstrated that chemogenetic inhibition of excitatory neurons in the mPFC and vHPC reduced anxiety levels in young epileptic mice. Altogether, our findings highlight the critical contributions of mPFC and vHPC to the pathogenesis of comorbid anxiety in epilepsy. These findings underscore the potential therapeutic significance of modulating the activity in these two regions as means to alleviate anxiety in a youth epilepsy population.

Ca2+-permeable AMPA receptor-dependent silencing of neurons by KCa3.1 channels during epileptiform activity.

Ca2+-activated KCa3.1 channels are known to contribute to slow afterhyperpolarization in pyramidal neurons of several brain areas, while Ca2+-permeable AMPA receptors (CP-AMPARs) may provide a subthreshold source of Ca2+ elevation in the cytoplasm. The functionality of these two types of channels has also been shown to be altered by epileptic disorders. However, the link between KCa3.1 channels and CP-AMPARs is poorly understood, and their potential interaction in epilepsy remains unclear. Here, we address this issue by overexpressing the KCNN4 gene, which encodes the KCa3.1 channel, using patch clamp, imaging, and channel blockers in an in vitro model of epilepsy in neuronal culture. We show that KCNN4 overexpression causes strong hyperpolarization and substantial silencing of neurons during epileptiform activity events, which also prevents KCNN4-positive neurons from firing action potentials (APs) during experimentally induced status epilepticus. Intracellular blocker application experiments showed that the amplitude of hyperpolarization was strongly dependent on CP-AMPARs, but not on NMDA receptors. Taken together, our data strongly suggest that subthreshold Ca2+ elevation produced by CP-AMPARs can trigger KCa3.1 channels to hyperpolarize neurons and protect them from seizures.

Flufenamic acid abolishes epileptiform activity in the entorhinal cortex slices by reducing the temporal summation of glutamatergic responses.

Flufenamic acid (FFA) is an anti-inflammatory drug that affects multiple targets and is a widely used research tool in ion channel studies. This pharmacological compound has a low level of selectivity for the transient receptor potential (TRP) channel superfamily, blocking calcium-activated nonselective cation current (ICAN) as well as afterdepolarizations (ADP) induced by it. A number of studies have demonstrated that FFA exerts an anti-epileptic effect in vitro, although the precise mechanism of this effect is not yet identified. The present study used whole-cell patch-clamp recordings and demonstrated that FFA (25 µM) can abolish the generation of seizure-like events (SLE) in entorhinal cortex slices perfused with a 4-aminopyridine-containing solution, depending on the time of application. FFA decreased the temporal summation of synaptic potentials at the onset of SLEs. However, as the epileptiform activity evolved and the SLE onset phase became more abrupt, the blocking effect of FFA diminished. FFA effectively abolished TRP channel-mediated slow ADPs, exerted a weak blockade and slowed the kinetics of GABAa receptor-mediated currents, and did not affect NMDA receptor-mediated evoked currents induced by extracellular stimulation. Although FFA did not directly inhibit NMDA receptor-mediated evoked currents, it decreased the summation of NMDA receptor-mediated potentials in a manner comparable to its effect on the initiation phase of SLE. This suggests that ICAN blockade may be responsible for this effect. Furthermore, our results showed that the selective blocker of melastatin TRP channels (TRPM4) 9-phenanthrol effectively abolished epileptiform activity in a manner analogous to FFA. In contrast, ML-204, the blocker of canonical TRP channels (TRPC), had no discernible effect on this phenomenon. In conclusion, the study demonstrate that FFA abolishes epileptiform activity in the entorhinal cortex by blocking TRPM4 channels and, consequently, decreasing the effectiveness of temporal summation of glutamatergic potentials.

Epileptic seizure prediction via multidimensional transformer and recurrent neural network fusion.

BACKGROUND: Epilepsy is a prevalent neurological disorder in which seizures cause recurrent episodes of unconsciousness or muscle convulsions, seriously affecting the patient's work, quality of life, and health and safety. Timely prediction of seizures is critical for patients to take appropriate therapeutic measures. Accurate prediction of seizures remains a challenge due to the complex and variable nature of EEG signals. The study proposes an epileptic seizure model based on a multidimensional Transformer with recurrent neural network(LSTM-GRU) fusion for seizure classification of EEG signals. METHODOLOGY: Firstly, a short-time Fourier transform was employed in the extraction of time-frequency features from EEG signals. Second, the extracted time-frequency features are learned using the Multidimensional Transformer model. Then, LSTM and GRU are then used for further learning of the time and frequency characteristics of the EEG signals. Next, the output features of LSTM and GRU are spliced and categorized using the gating mechanism. Subsequently, seizure prediction is conducted. RESULTS: The model was tested on two datasets: the Bonn EEG dataset and the CHB-MIT dataset. On the CHB-MIT dataset, the average sensitivity and average specificity of the model were 98.24% and 97.27%, respectively. On the Bonn dataset, the model obtained about 99% and about 98% accuracy on the binary classification task and the tertiary upper classification task, respectively. CONCLUSION: The findings of the experimental investigation demonstrate that our model is capable of exploiting the temporal and frequency characteristics present within EEG signals.