Multi-scale modelling of the epileptic brain: advantages of computational therapy exploration.

Objective: Epilepsy is a complex disease spanning across multiple scales, from ion channels in neurons to neuronal circuits across the entire brain. Over the past decades, computational models have been used to describe the pathophysiological activity of the epileptic brain from different aspects. Traditionally, each computational model can aid in optimizing therapeutic interventions, therefore, providing a particular view to design strategies for treating epilepsy. As a result, most studies are concerned with generating specific models of the epileptic brain that can help us understand the certain machinery of the pathological state. Those specific models vary in complexity and biological accuracy, with system-level models often lacking biological details.Approach: Here, we review various types of computational model of epilepsy and discuss their potential for different therapeutic approaches and scenarios, including drug discovery, surgical strategies, brain stimulation, and seizure prediction. We propose that we need to consider an integrated approach with a unified modelling framework across multiple scales to understand the epileptic brain. Our proposal is based on the recent increase in computational power, which has opened up the possibility of unifying those specific epileptic models into simulations with an unprecedented level of detail.Main results: A multi-scale epilepsy model can bridge the gap between biologically detailed models, used to address molecular and cellular questions, and brain-wide models based on abstract models which can account for complex neurological and behavioural observations.Significance: With these efforts, we move toward the next generation of epileptic brain models capable of connecting cellular features, such as ion channel properties, with standard clinical measures such as seizure severity.

Emerging ferroelectric materials ScAlN: applications and prospects in memristors.

The research found that after doping with rare earth elements, a large number of electrons and holes will be produced on the surface of AlN, which makes the material have the characteristics of spontaneous polarization. A new type of ferroelectric material has made a new breakthrough in the application of nitride-materials in the field of integrated devices. In this paper, the application prospects and development trends of ferroelectric material ScAlN in memristors are reviewed. Firstly, various fabrication processes and structures of the current ScAlN thin films are described in detail to explore the implementation of their applications in synaptic devices. Secondly, a series of electrical properties of ScAlN films, such as the current switching ratio and long-term cycle durability, were tested to explore whether their electrical properties could meet the basic needs of memristor device materials. Finally, a series of summaries on the current research studies of ScAlN thin films in the synaptic simulation are made, and the working state of ScAlN thin films as a synaptic device is observed. The results show that the ScAlN ferroelectric material has high residual polarization, no wake-up function, excellent stability and obvious STDP behavior, which indicates that the modified material has wide application prospects in the research and development of memristors.

A 4.43 TΩ ZIN 0.0128 mm2 Cascaded Instrumentation Amplifier with Input-biased Pseudo Resistor for Implantable Brain Machine Interfaces.

A cascaded instrumentation amplifier (CaIA) with input-biased pseudo resistors (IBPR) is presented for implantable brain machine interfaces (BMI). The gain distribution of two-stage cascaded amplifiers, instead of a single-stage amplifier, helps to achieve an input impedance of 4.43TΩ at 100Hz, and maintain the small active area (0.0128 mm2). The input-biased pseudo resistors contribute to a much lower high-pass corner (fHP=0.00011Hz) compared with the conventional structure, the input-referred noise is only 3.836µVrms integrated from 0.5Hz to 10kHz with 0.98µW power consumption.Clinical Relevance- This establishes an area-efficient amplifier design with ultra-high input impedance (4.43TΩ at 100Hz) and hyper-low high-pass corner frequency (fHP=0.00011Hz), which is suitable for long-term monitoring of neural activities (including slow oscillations) in implantable brain-machine interfaces.

Brainprint Recognition Based on the Stable SSVEP Space-Frequency Energy Distribution.

Brainprint recognition has received increasing attention in information security. Electroencephalography (EEG) signals measured under task-related or task-free conditions have been exploited as brain biometrics. However, what components make the uniqueness of one's brain signals remains unclear. In this study, we proposed an interpretable biomarker based on steady-state visual evoked potentials (SSVEP) signals for EEG biometric identification. Firstly, we recovered pure SSVEP components from EEG by a point-position equivalent reconstruction (PPER) method. Then, we calculated the distribution properties of SSVEP components in space and frequency. By using the uniform manifold approximation and projection, we reduced the distribution features to 2-dimensions, which shows the separability of the subjects. Lastly, we built a long short-term memory (LSTM) network to perform brainprint recognition on the SSVEP benchmark dataset. The average recognition accuracy can reach up to 98.33%. Our results demonstrate that the space-frequency energy feature of SSVEP is an effective and interpretable biomarker for brainprint recognition. This study provides a further understanding of the uniqueness of individual EEG signal, and facilitates its potential application for personal identification.

Unsupervised Feature Representation of Sleep EEG Data with Transient Deep Boltzmann Machine.

The supervised sleep staging methods are challenged by their strict requirements of a labelled and large dataset. This study considers an unsupervised dimensionality reduction method, the Deep Boltzmann Machine (DBM), trained to a transient state for binary classification of sleep stages. First, the joint time-frequency domain features from the polysomnographic recordings are extracted. Second, the extracted features are smoothed using 2 min rolling window to include contextual temporal information, and finally, they serve as an input for unsupervised training of DBM_transient. The results show that our method effectively separates the sleep stages in two-dimensional feature space with a large Fisher's discriminant value. The classification performance by the DBM_transient achieves a 96.1% F1 score, which is higher than DBM converged to an equilibrium state (95.2%), Principal Component Analysis (92.5%), Isometric Feature Mapping (95.9%), t-distributed Stochastic Neighbor Embedding (94.9%), and Uniform Manifold Approximation (95.0%) on the widely used sleep-EDF database. Additionally, Fisher's discriminant function demonstrates the superiority of the DBM_transient. The significance of the DBM transient lies in its ease of interpretability in two-dimensional space, and future multi-class implementation of the method may facilitate its usage in clinical applications.

The role of age-related sleep EEG changes in memory decline: experiments and computational modeling.

The slow oscillation (SO) observed during deep sleep is known to facilitate memory consolidation. However, the impact of age-related changes in sleep electroencephalography (EEG) oscillations and memory remains unknown. In this study, we aimed to investigate the contribution of age-related changes in sleep SO and its role in memory decline by combining EEG recordings and computational modeling. Based on the detected SO events, we found that older adults exhibit lower SO density, lower SO frequency, and longer Up and Down state durations during N3 sleep compared to young and middle-aged groups. Using a biophysically detailed thalamocortical network model, we simulated the "aged" brain as a partial loss of synaptic connections between neurons in the cortex. Our simulations showed that the changes in sleep SO properties in the "aged" brain, similar to those observed in older adults, resulting in impaired memory consolidation. Overall, this study provides mechanistic insights into how age-related changes modulate sleep SOs and memory decline.Clinical Relevance- This study contributes towards finding feasible biomarkers and target mechanism for designing therapy in older adults with memory deficits, such as Alzheimer's disease patients.

Benchmarking EEG-based Cross-dataset Driver Drowsiness Recognition with Deep Transfer Learning.

It usually takes a long time to collect data for calibration when using electroencephalography (EEG) for driver drowsiness monitoring. Cross-dataset recognition is desirable since it can significantly save the calibration time when an existing dataset is used. However, the recognition accuracy is affected by the distribution drift problem caused by different experimental environments when building different datasets. In order to solve the problem, we propose a deep transfer learning model named Entropy-Driven Joint Adaptation Network (EDJAN), which can learn useful information from source and target domains simultaneously. An entropy-driven loss function is used to promote clustering of target-domain representations and an individual-level domain adaptation technique is proposed to alleviate the distribution discrepancy problem of test subjects. We use two public driving datasets SEEG-VIG and SADT to test the model on the cross-dataset setting. The proposed model achieved an accuracy of 83.3% when SADT is used as source domain and SEED-VIG is used as target domain and 76.7% accuracy on the reverse setting, which is higher than the other SOTA methods. The results are further analyzed with both global and local interpretation methods. Our work illuminates a promising direction of using EEG for calibration-free driver drowsiness recognition.

A physical understanding and quantification for the regulation of orexin on sleep.

The orexinergic neurons located in the lateral hypothalamus play a vital role in maintaining wakefulness and regulating sleep stability. Previous research has demonstrated that the absence of orexin (Orx) can trigger narcolepsy, a condition characterized by frequent shifts between wakefulness and sleep. However, the specific mechanisms and temporal patterns through which Orx regulates wakefulness/sleep are not fully understood. In this study, we developed a new model that combines the classical Phillips-Robinson sleep model with the Orx network. Our model incorporates a recently discovered indirect inhibition of Orx on sleep-promoting neurons in the ventrolateral preoptic nucleus. By integrating appropriate physiological parameters, our model successfully replicated the dynamic behavior of normal sleep under the influence of circadian drive and homeostatic processes. Furthermore, our results from the new sleep model unveiled two distinct effects of Orx: excitation of wake-active neurons and inhibition of sleep-active neurons. The excitation effect helps to sustain wakefulness, while the inhibition effect contributes to arousal, consistent with experimental findings [De Luca et al., Nat. Commun. 13, 4163 (2022)]. Moreover, we utilized the theory of potential landscapes to investigate the physical mechanisms underlying the frequent transitions observed in narcolepsy. The topography of the underlying landscape delineated the brain's capacity to transition between different states. Additionally, we examined the impact of Orx on barrier height. Our analysis demonstrated that a reduced level of Orx led to a bistable state with an extremely low threshold, contributing to the development of narcoleptic sleep disorder.

Unsupervised Feature Representation Based on Deep Boltzmann Machine for Seizure Detection.

The Electroencephalogram (EEG) pattern of seizure activities is highly individual-dependent and requires experienced specialists to annotate seizure events. It is clinically time-consuming and error-prone to identify seizure activities by visually scanning EEG signals. Since EEG data are heavily under-represented, supervised learning techniques are not always practical, particularly when the data is not sufficiently labelled. Visualization of EEG data in low-dimensional feature space can ease the annotation to support subsequent supervised learning for seizure detection. Here, we leverage the benefit of both the time-frequency domain features and the Deep Boltzmann Machine (DBM) based unsupervised learning techniques to represent EEG signals in a 2-dimensional (2D) feature space. A novel unsupervised learning approach based on DBM, namely DBM_transient, is proposed by training DBM to a transient state for representing EEG signals in a 2D feature space and clustering seizure and non-seizure events visually. The effectiveness of DBM_transient is demonstrated on a widely-used benchmark dataset from Bonn University (Bonn dataset) and a raw clinical dataset from Chinese 301 Hospital (C301 dataset), with a large fisher discriminant value, surpassing the abilities of other dimensionality reduction methods, including DBM converged to an equilibrium state, Kernel Principal Component Analysis, Isometric Feature Mapping, t-distributed Stochastic Neighbour Embedding, Uniform Manifold Approximation. Such feature representation and visualization can help physicians to understand better the normal versus epileptic brain activities of each patient and thus enhance their diagnosis and treatment abilities. The significance of our approach facilitates its future usage in clinical applications.

LRRK2 Deficiency Aggravates Sleep Deprivation-Induced Cognitive Loss by Perturbing Synaptic Pruning in Mice.

Mutations of the leucine-rich repeat kinase 2 (LRRK2) gene are associated with pronounced sleep disorders or cognitive dysfunction in neurodegenerative diseases. However, the effects of LRRK2 deficiency on sleep rhythms and sleep deprivation-related cognitive changes, and the relevant underlying mechanism, remain unrevealed. In this study, Lrrk2-/- and Lrrk2+/+ mice were subjected to normal sleep (S) or sleep deprivation (SD). Sleep recording, behavioral testing, Golgi-cox staining, immunofluorescence, and real-time PCR were employed to evaluate the impacts of LRRK2 deficiency on sleep behaviors and to investigate the underlying mechanisms. The results showed that after SD, LRRK2-deficient mice displayed lengthened NREM and shortened REM, and reported decreased dendritic spines, increased microglial activation, and synaptic endocytosis in the prefrontal cortex. Meanwhile, after SD, LRRK2 deficiency aggravated cognitive impairments, especially in the recall memory cued by fear conditioning test. Our findings evidence that LRRK2 modulates REM/NREM sleep and its deficiency may exacerbate sleep deprivation-related cognitive disorders by perturbing synaptic plasticity and microglial synaptic pruning in mice.

Significant Low-Dimensional Spectral-Temporal Features for Seizure Detection.

Absence seizure as a generalized onset seizure, simultaneously spreading seizure to both sides of the brain, involves around ten-second sudden lapses of consciousness. It common occurs in children than adults, which affects living quality even threats lives. Absence seizure can be confused with inattentive attention-deficit hyperactivity disorder since both have similar symptoms, such as inattention and daze. Therefore, it is necessary to detect absence seizure onset. However, seizure onset detection in electroencephalography (EEG) signals is a challenging task due to the non-stereotyped seizure activities as well as their stochastic and non-stationary characteristics in nature. Joint spectral-temporal features are believed to contain sufficient and powerful feature information for absence seizure detection. However, the resulting high-dimensional features involve redundant information and require heavy computational load. Here, we discover significant low-dimensional spectral-temporal features in terms of mean-standard deviation of wavelet transform coefficient (MS-WTC), based on which a novel absence seizure detection framework is developed. The EEG signals are transformed into the spectral-temporal domain, with their low-dimensional features fed into a convolutional neural network. Superior detection performance is achieved on the widely-used benchmark dataset as well as a clinical dataset from the Chinese 301 Hospital. For the former, seven classification tasks were evaluated with the accuracy from 99.8% to 100.0%, while for the latter, the method achieved a mean accuracy of 94.7%, overwhelming other methods with low-dimensional temporal and spectral features. Experimental results on two seizure datasets demonstrate reliability, efficiency and stability of our proposed MS-WTC method, validating the significance of the extracted low-dimensional spectral-temporal features.

[Molecular mechanism of Qishen Yiqi Dripping Pills in treating myocardial ischemia:a study based on HIF-1 signaling pathway].

Qishen Yiqi Dripping Pills(QSYQ) are used clinically to treat various myocardial ischemic diseases, such as angina pectoris, myocardial infarction, and heart failure; however, the molecular mechanism of QSYQ remains unclear, and the scientific connotation of traditional Chinese medicine(TCM) compatibility has not been systematically explained. The present study attempted to screen the critical pathway of QSYQ in the treatment of myocardial ischemia by network pharmacology and verify the therapeutic efficacy with the oxygen-glucose deprivation(OGD) model, in order to reveal the molecular mechanism of QSYQ based on the critical pathway. The key targets of QSYQ were determined by active ingredient identification and target prediction, and underwent pathway enrichment analysis and functional annotation with David database to reveal the biological role and the critical pathway of QSYQ. Cell counting Kit-8(CCK-8), lactate dehydrogenase(LDH), and Western blot tests were launched on high-content active ingredients with OGD cell model to reveal the molecular mechanism of QSYQ based on the critical pathway. The results of network pharmacology indicated that QSYQ, containing 18 active ingredients and 82 key targets, could protect cardiomyocytes by regulating biological functions, such as nitric oxide biosynthesis, apoptosis, inflammation, and angiogenesis, through TNF signaling pathway, HIF-1 signaling pathway, PI3 K-Akt signaling pathway, etc. HIF-1 signaling pathway was the critical pathway. As revealed by CCK-8 and LDH tests, astragaloside Ⅳ, salvianic acid A, and ginsenoside Rg_1 in QSYQ could enhance cell viability and reduce LDH in the cell supernatant in a concentration-dependent manner(P<0.05). As demonstrated by the Western blot test, astragaloside Ⅳ significantly down-regulated the protein expression of serine/threonine-protein kinase(Akt1) and hypoxia-inducible factor 1α(HIF-1α) in the HIF-1 signaling pathway, and up-regulated the protein expression of vascular endothelial growth factor A(VEGFA). Salvianic acid A significantly down-regulated the protein expression of upstream phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha(PIK3 CA) and downstream HIF-1α of Akt1. Ginsenoside Rg_1 significantly down-regulated the expression of HIF-1α protein and up-regulated the expression of VEGFA. The therapeutic efficacy of QSYQ on myocardial ischemia was achieved by multiple targets and multiple pathways, with the HIF-1 signaling pathway serving as the critical one. The active ingredients of QSYQ could protect cardiomyocytes synergistically by regulating the targets in the HIF-1 signaling pathway to inhibit its expression.

Conditions for the genesis of early afterdepolarization in a model of a ventricular myocyte.

Early afterdepolarization (EAD) is a major arrhythmogenic factor in the long QT syndrome (LQTS), whose conditions for genesis have puzzled people for several decades. Here, we employ the phase I Luo-Rudy ventricular myocyte model to investigate EAD using methods from nonlinear dynamics and provide valuable insights into EAD genesis from a physical perspective. Two major results are obtained: (i) Sufficient parametric conditions for EAD are analytically determined and then used to analyze in detail the effects of the physiological parameters. (ii) The normal form of the Hopf bifurcation that leads to EAD is derived and then used to determine whether the Hopf bifurcation is subcritical or supercritical for EAD genesis and the corresponding amplitude and period of the EAD oscillation. Our work here paves the way for further studies of more complicated multi-scale dynamics of EAD and may lead to effective treatments for LQTS arrhythmias.

Active anti-corrosion of epoxy coating by nitrite ions intercalated MgAl LDH.

Layered double hydroxide (LDH) with NO2- intercalation was successfully prepared via acidification oscillation and ion exchange. The nano-fillers were incorporated into the resin to prepare anti-corrosion coatings with the thickness of ca. 50 ±â€¯5 µm. The electrochemical and self-repairing properties of the LDH-doped coatings were studied by EIS and LEIS. Results indicated that the addition of LDH loaded with nitrite induced obvious increased in the impedance of coating (from 4.64 × 108 Ω cm2 to 2.14 × 1010 Ω cm2) and improved the anticorrosion performance of the coating. In addition, the localized corrosion of coatings could be largely inhibited, and the released nitrite ions from LDH interlayers exhibited active anticorrosion functions. When LDH nanosheets were added to the coatings, the lamella structures improved the barrier performances of the coatings. At the same time, the excellent ion exchanges ability of LDH could be used as storage stations for chloride ions, and the release of nitrite ions could play an active anti-corrosion role. Both of them cooperated to synergistically improve the anti-corrosion performance of the coating.

The Protective Effect of Different Polar Solvent Extracts of Er Miao San on Rats with Adjuvant Arthritis.

OBJECTIVE: The aim of this study was to evaluate the antiarthritic effects of different polar solvent extracts of Er Miao San (EMS) on model rats with adjuvant arthritis (AA) and screen the effective pats of EMS in the treatment of arthritis. METHODS: Four different polar solvent extracts of EMS such as petroleum ether (PE), methylene chloride (CH2Cl2), ethyl acetate (EtOAc), and n-butanol (n-butanol (. RESULTS: Administration of EtOAc and CH2Cl2 parts remarkably inhibited the paw swelling, decreased the index of arthritis, decreased the body weight loss, and improved the changes of histopathology. Furthermore, the concentrations of proinflammatory cytokines (TNF-α, IL-1ß, and IL-6) were significantly lower, while the anti-inflammatory cytokine (IL-10) was remarkably higher compared with that in the model group. And the result of UHPLC analysis indicated that the effective parts of EMS contain berberine and atractylodin. CONCLUSIONS: EtOAc and CH2Cl2 are the effective parts of EMS that can improve arthritis. In particular, berberine and atractylodin may be responsible for the antiarthritic activity of EMS. This research provided pharmacological and chemical foundation for the application of EMS in treating rheumatoid arthritis (RA).

Er Miao San, a traditional Chinese herbal formula, attenuates complete Freund's adjuvant-induced arthritis in rats by regulating Th17/Treg cells.

Context: Er Miao San (EMS) is a traditional Chinese medicine composed of Atractylodis Rhizoma and Phellodendri Cortex in a 1:1 weight ratio. EMS has been used to treat rheumatism in China for many years.Objective: To evaluate the anti-arthritic activity of EMS extract on adjuvant-induced arthritis (AA) in Sprague-Dawley rats and to clarify its mechanisms of action.Materials and methods: EMS (0.75, 1.5 and 3 g/kg, once daily) was orally administered from day 18 after immunization to day 31. The effects of EMS on AA rats were evaluated by histopathological examination, paw swelling and polyarthritis index. The proliferation of fibroblast-like synoviocyte (FLS) and T cells was detected by CCK-8. The percentages of Th17 cells and Treg cells in splenocytes were determined by flow cytometry. Levels of cytokines in serum were detected by ELISA.Results: EMS treatment significantly decreased the paw volume (from 1.20 to 0.81), polyarthritis index (from 9.56 to 4.46) and alleviated ankle joint histopathology in AA rats. EMS inhibited the proliferation of FLS and T cells. Furthermore, EMS treatment decreased Th17 cells (from 4.62 to 2.08%) and increased Treg cells (from 2.77 to 4.75%) in splenocytes. The levels of IL-17A, TNF-α and IL-6 were remarkably decreased in the serum of EMS-treated rats, whereas the levels of IL-10 and TGF-ß1 were significantly increased.Conclusions: EMS exhibits anti-arthritic activity in the AA model by regulating the balance of cytokines and the ratio of Th17 and Treg cells. These insights may provide an experimental basis for the clinical treatment of RA.

A high-efficiency corrosion inhibitor of N-doped citric acid-based carbon dots for mild steel in hydrochloric acid environment.

Novel N-doped carbon dots (CDs) were obtained through pyrolysis of ammonium citrate at 180 °C for 1, 2 and 3 h, and their corrosion inhibition effect on Q235 steel in 1 M HCl solution were evaluated through electrochemical impedance spectroscopy (EIS), potentiodynamic polarization (Tafel), scanning vibrating electrode technique (SVET) analysis. The changes of corrosion current density and impedance modulus of Q235 steel in inhibitor solutions showed that the as-prepared carbon dots presented a valid protective effect on steel in 1 M HCl solution. Meanwhile, the inhibition efficiency of three carbon dots exceeded 90% at 200 mg/L and the highest inhibitive efficiency was found for the carbon dots prepared at the reaction time of 2 h. The adsorption mechanism of all as-prepared carbon dots complied with the Langmuir adsorption model, containing chemical and physical adsorptions, which was also confirmed by X-ray photoelectronic spectroscopy (XPS) analysis.

Anti-Inflammatory Effects of Different Elution Fractions of Er-Miao-San on Acute Inflammation Induced by Carrageenan in Rat Paw Tissue.

BACKGROUND Er-Miao-San (EMS) is used in traditional Chinese medicine. This study aimed to investigate the effect of different elution fractions of EMS on acute inflammation induced by carrageenan in the rat paw and the possible mechanisms of action. MATERIAL AND METHODS Different aqueous fractions of EMS added to an AB-8 macroporous resin column and eluted with 0, 30%, 60%, and 90% ethanol. The content of berberine was evaluated by ultra-performance liquid chromatography (UPLC). Following injection of carrageenan and elution fractions of EMS into the rat paw, the volume of edema, levels of prostaglandin E2 (PGE2), tumor necrosis factor-alpha (TNF-alpha), interleukin (IL)-1ß, and IL-10 in the rat tissue were quantified by enzyme-linked immunosorbent assay (ELISA). Myeloperoxidase (MPO) activity and nitric oxide (NO) levels were measured by spectrophotometry. RESULTS The 60% and 90% ethanol elution fractions of EMS contained berberine, and both inhibited edema after carrageenan injection, with inhibitory rates of 31.04-40.86% and 48.84-52.18%, respectively, and with a significant reduction in MPO activity and NO production. The 60% ethanol elution fraction of EMS significantly decreased IL-1ß levels and increased IL-10 levels, and the 30%, 60%, and 90% ethanol EMS elution fractions considerably reduced the levels of TNF-alpha. The 60% and 90% ethanol EMS elution fractions significantly reduced PGE2 levels in the rat paw. CONCLUSIONS The 60% and 90% ethanol elution fractions of EMS had an anti-inflammatory effect following injection of carrageenan in the rat paw.

Unified analysis of global and focal aspects of absence epilepsy via neural field theory of the corticothalamic system.

Absence epilepsy is characterized by a sudden paroxysmal loss of consciousness accompanied by oscillatory activity propagating over many brain areas. Although primary generalized absence seizures are supported by the global corticothalamic system, converging experimental evidence supports a focal theory of absence epilepsy. Here a physiology-based corticothalamic model is investigated with spatial heterogeneity due to focal epilepsy to unify global and focal aspects of absence epilepsy. Numeric and analytic calculations are employed to investigate the emergent spatiotemporal dynamics as well as their underlying dynamical mechanisms. They can be categorized into three scenarios: suppressed epilepsy, focal seizures, or generalized seizures, as summarized from a phase diagram vs focal width and characteristic axon range. The corresponding temporal frequencies and spatial extents of cortical waves in generalized seizures and focal seizures agree well with experimental observations of global and focal aspects of absence epilepsy, respectively. The emergence of the spatiotemporal dynamics corresponding to focal seizures provides a biophysical explanation of the temporally higher frequency but spatially more localized cortical waves observed in genetic rat models that display characteristics of human absence epilepsy. Predictions are also presented for further experimental test.