Enhanced PET imaging using progressive conditional deep image prior.

Objective.Unsupervised learning-based methods have been proven to be an effective way to improve the image quality of positron emission tomography (PET) images when a large dataset is not available. However, when the gap between the input image and the target PET image is large, direct unsupervised learning can be challenging and easily lead to reduced lesion detectability. We aim to develop a new unsupervised learning method to improve lesion detectability in patient studies.Approach.We applied the deep progressive learning strategy to bridge the gap between the input image and the target image. The one-step unsupervised learning is decomposed into two unsupervised learning steps. The input image of the first network is an anatomical image and the input image of the second network is a PET image with a low noise level. The output of the first network is also used as the prior image to generate the target image of the second network by iterative reconstruction method.Results.The performance of the proposed method was evaluated through the phantom and patient studies and compared with non-deep learning, supervised learning and unsupervised learning methods. The results showed that the proposed method was superior to non-deep learning and unsupervised methods, and was comparable to the supervised method.Significance.A progressive unsupervised learning method was proposed, which can improve image noise performance and lesion detectability.

A spectral sampling algorithm in dynamic causal modelling for resting-state fMRI.

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely utilized to study the directed influences among neural populations which were called effective connectivity (EC), and the spectral dynamic causal modelling (spDCM) is the state-of-the-art framework to identify them. However, spDCM used variational Laplace to approximate the posterior density by maximizing the free energy, which might underestimate the variability of posterior density and get locked to the local minima. A spectral sampling algorithm (SS-DCM) was proposed to improve the estimation accuracy of the dynamic causal model for rs-fMRI. In SS-DCM, a naïve Bayesian model was constructed in the spectral domain, which described the probabilistic relationship between the sampled parameters and cross spectra of the observed blood oxygen level-dependent signals, and the parameters were sampled using randomly walked Markov Chain Monto Carlo scheme. The root mean square errors of the estimation of EC and hemodynamic parameters of SS-DCM, spDCM and generalized filter scheme were compared in the synthetic data, and SS-DCM was the most accurate and stable. A comparative evaluation using empirical rs-fMRI data was performed to study the EC pattern of the default mode network and compare the accuracy of classification between typically developed subjects and inattentive attention deficit and hyperactivity disorder patients. The results showed high consistency of positivity and negativity of EC between spDCM and SS-DCM, and SS-DCM also provided higher classification accuracy. It is highlighted that SS-DCM improves the accuracy of the estimation of EC and provides accurate information of discrepancies between diseased and healthy subjects using rs-fMRI.

Automatic segmentation of thrombosed aortic dissection in post-operative CT-angiography images.

PURPOSE: The thrombus in the false lumen (FL) of aortic dissection (AD) patients is a meaningful indicator to determine aortic remodeling but difficult to measure in clinic. In this study, a novel segmentation strategy based on deep learning was proposed to automatically extract the thrombus in the FL in post-operative computed tomography angiography (CTA) images of AD patients, which provided an efficient and convenient segmentation method with high accuracy. METHODS: A two-step segmentation strategy was proposed. Each step contained a convolutional neural network (CNN) to segment the aorta and the thrombus, respectively. In the first step, a CNN was used to obtain the binary segmentation mask of the whole aorta. In the second step, another CNN was introduced to segment the thrombus. The results of the first step were used as additional input to the second step to highlight the aorta in the complex background. Moreover, skip connection attention refinement (SAR) modules were designed and added in the second step to improve the segmentation accuracy of the thrombus details by efficiently using the low-level features. RESULTS: The proposed method provided accurate thrombus segmentation results (0.903 ± 0.062 in dice score, 0.828 ± 0.092 in Jaccard index, and 2.209 ± 2.945 in 95% Hausdorff distance), which showed improvement compared to the methods without prior information (0.846 ± 0.085 in dice score) and the method without SAR (0.899 ± 0.060 in dice score). Moreover, the proposed method achieved 0.967 ± 0.029 and 0.948 ± 0.041 in dice score of true lumen (TL) and patent FL (PFL) segmentation, respectively, indicating the excellence of the proposed method in the segmentation task of the overall aorta. CONCLUSIONS: A novel CNN-based segmentation framework was proposed to automatically obtain thrombus segmentation for thrombosed AD in post-operative CTA images, which provided a useful tool for further application of thrombus-related indicators in clinical and research application.

Volumetric Measurements Improve the Accuracy of Aortic Remodeling Prediction in Aortic Dissection.

Accessing aortic remodeling status through regular follow-ups is essential for acute type A aortic dissection patients undergone surgical treatment. Aortic remodeling status was usually determined using diameter or area measurements of the true and false lumen in specific anatomical slices of medical images. However, these indicators only represent partial information about the aorta and can hardly characterize the overall aorta situation. In this study, we included two types of morphology features collected from computed tomography angiography images to predict the aortic remodeling. One type is the volumetric measurements of the true and false lumen, which provide a better overall description of the aorta, and the other type is the volumetric measurements of the thrombus in false lumen and the patent false lumen, which present more detailed information of the dissection. Through progressively incorporating these measurements into the construction of the remodeling prediction model, we investigated the importance of the features that describe the overall situation and that characterize aortic internal details in remodeling prediction, especially the effect of quantitative thrombosis features. The results showed that with the inclusion of the two types of volume features, the prediction accuracy of the model increased, which proves that volumetric measurements of aortic dissection, especially the volume of thrombus, are of significant value in aortic remodeling prediction, and should be paid more attention on in clinical practice and research areas. Clinical Relevance-Demonstrating the importance of volumetric measurements of true and false lumen thrombus in false lumen and patent false lumen in the prediction of aortic remodeling.

Case Report: Stereoelectroencephalography and Stereoelectroencephalography-Guided Radiofrequency Thermocoagulation in Familial Lateral Temporal Lobe Epilepsy.

Familial lateral temporal lobe epilepsy (FLTLE) is genetic focal epilepsy usually characterised by auditory symptoms. Most FLTLE cases can be controlled by anti-seizure medications, and to our best knowledge, there are no previous reports about stereoelectroencephalography (SEEG) used for patients with FLTLE. In this report, we present two patients with FLTLE in one family and their SEEG performances, together with 18F-fluorodeoxyglucose (18F-FDG) PET and MRI results. In case 1, fast activities originated from the right superior temporal gyrus and spread rapidly to the right anterior insular lobe and hippocampus. In case 2, there were two seizure patterns: (1) The fast activities or sharp slow waves were identified at the left superior temporal gyrus, then, sharp waves and spike waves spread in the left superior temporal gyrus; (2) There were fast activities and slow-wave oscillation originated in the left superior temporal gyrus, then, the fast activities spread in the left superior temporal gyrus and finally spread to the other sites. An SEEG-guided radiofrequency thermocoagulation was performed for both patients and one of them underwent resection surgery. Seizures are well-controlled and the patients are very satisfied with the therapeutic effects.

Efficacy of cathodal transcranial direct current stimulation on electroencephalographic functional networks in patients with focal epilepsy: Preliminary findings.

BACKGROUND: Neuromodulation is a promising therapeutic alternative for epilepsy. We aimed to explore the efficacy and safety of cathodal transcranial current direct stimulation (ctDCS) on electroencephalographic functional networks in focal epilepsy. METHODS: A sham-controlled, double-blinded, randomized study was conducted on 25 participants with focal epilepsy who underwent a 5-day, -1.0 mA, 20 min ctDCS, which targeted at the most active interictal epileptiform discharge (IED) region. We examined the electroencephalograms (EEGs) at baseline, immediately and at 4 weeks following ctDCS. The graph theory-based brain networks were established through time-variant partial directed coherence (TVPDC), and were calculated between each pair of EEG signals. The functional networks were characterized using average clustering coefficient, characteristic path length, and small-worldness index. The seizure frequencies, IEDs, graph-theory metrics and cognitive tests were compared. RESULTS: Preliminary findings indicated an IED reduction of 30.2% at the end of 5-day active ctDCS compared to baseline (p < 0.10) and a significant IED reduction of 33.4% 4 weeks later (p < 0.05). In terms of the EEG functional network, the small-worldness index significantly reduced by 3.5% (p < 0.05) and the characteristic path length increased by 1.8% (p < 0.10) at the end of the session compared to the baseline. No obvious change was found in the seizure frequency during follow-up (p > 0.05). The Mini-Mental State Examination (MMSE) showed no difference between the active and sham groups (p > 0.05). No severe adverse reactions were observed. CONCLUSIONS: In focal epilepsy, the 5-day consecutive ctDCS may potentially decrease the IEDs and ameliorate the EEG functional network, proposing a novel personalized therapeutic scenario for epilepsy.

Functional Network Alterations as Markers for Predicting the Treatment Outcome of Cathodal Transcranial Direct Current Stimulation in Focal Epilepsy.

BACKGROUND AND PURPOSE: Transcranial direct current stimulation (tDCS) is an emerging non-invasive neuromodulation technique for focal epilepsy. Because epilepsy is a disease affecting the brain network, our study was aimed to evaluate and predict the treatment outcome of cathodal tDCS (ctDCS) by analyzing the ctDCS-induced functional network alterations. METHODS: Either the active 5-day, -1.0 mA, 20-min ctDCS or sham ctDCS targeting at the most active interictal epileptiform discharge regions was applied to 27 subjects suffering from focal epilepsy. The functional networks before and after ctDCS were compared employing graph theoretical analysis based on the functional magnetic resonance imaging (fMRI) data. A support vector machine (SVM) prediction model was built to predict the treatment outcome of ctDCS using the graph theoretical measures as markers. RESULTS: Our results revealed that the mean clustering coefficient and the global efficiency decreased significantly, as well as the characteristic path length and the mean shortest path length at the stimulation sites in the fMRI functional networks increased significantly after ctDCS only for the patients with response to the active ctDCS (at least 20% reduction rate of seizure frequency). Our prediction model achieved the mean prediction accuracy of 68.3% (mean sensitivity: 70.0%; mean specificity: 67.5%) after the nested cross validation. The mean area under the receiver operating curve was 0.75, which showed good prediction performance. CONCLUSION: The study demonstrated that the response to ctDCS was related to the topological alterations in the functional networks of epilepsy patients detected by fMRI. The graph theoretical measures were promising for clinical prediction of ctDCS treatment outcome.

A multicompartment model for intratumor tissue-specific analysis of DCE-MRI using non-negative matrix factorization.

PURPOSE: A pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data is subject to inaccuracy and instability partly owing to the partial volume effect (PVE). We proposed a new multicompartment model for a tissue-specific pharmacokinetic analysis in DCE-MRI data to solve the PVE problem and to provide better kinetic parameter maps. METHODS: We introduced an independent parameter named fractional volumes of tissue compartments in each DCE-MRI pixel to construct a new linear separable multicompartment model, which simultaneously estimates the pixel-wise time-concentration curves and fractional volumes without the need of the pure-pixel assumption. This simplified convex optimization model was solved using a special type of non-negative matrix factorization (NMF) algorithm called the minimum-volume constraint NMF (MVC-NMF). RESULTS: To test the model, synthetic datasets were established based on the general pharmacokinetic parameters. On well-designed synthetic data, the proposed model reached lower bias and lower root mean square fitting error compared to the state-of-the-art algorithm in different noise levels. In addition, the real dataset from QIN-BREAST-DCE-MRI was analyzed, and we observed an improved pharmacokinetic parameter estimation to distinguish the treatment response to chemotherapy applied to breast cancer. CONCLUSION: Our model improved the accuracy and stability of the tissue-specific estimation of the fractional volumes and kinetic parameters in DCE-MRI data, and improved the robustness to noise, providing more accurate kinetics for more precise prognosis and therapeutic response evaluation using DCE-MRI.

Localization of the epileptogenic zone based on ictal stereo-electroencephalogram: Brain network and single-channel signal feature analysis.

Accurate localization of the epileptogenic zone (EZ) is crucial for refractory focal epilepsy patients to achieve freedom from seizures following epilepsy surgery. In this study, ictal stereo-electroencephalography data from 35 patients with refractory focal epilepsy were analyzed. Effective networks based on partial directed coherence were analyzed, and a gray level co-occurrence matrix was applied to extract the time-varying features of the in-degree. These features, combined with the single-channel signal time-frequency features, including approximate entropy and line length, were used to localize the EZ based on a cluster algorithm. For all seizure-free patients (n = 23), the proposed method was effective in identifying the clinical-EZ-contacts and clinical-EZ-blocks, with an F1-score of 62.47 % and 72.18 %, respectively. The sensitivity was 96.00 % for the clinical-EZ-block identification, which provided the information for the decision-making of clinicians, prompting clinicians to focus on the identified EZ-blocks and their nearby contacts. The agreement between the EZ identified by the proposed method and the clinical-EZ was worse for non-seizure-free patients (n = 12) than for seizure-free patients. Furthermore, our method provided better results than using only brain network or single-channel signal features. This suggests that combining these complementary features can facilitate more accurate localization of the EZ.

The role of the substantia nigra pars reticulata anterior in amygdala-kindled seizures.

BACKGROUND: It has been reported that the substantia nigra pars reticulata (SNr) is of regional differences and involved in the initiation, generalization, and cessation of seizures. However, neuropharmacological investigations into the role of the SNr anterior (SNra) in temporal lobe epilepsy (TLE) have been inconsistent, suggesting that electrophysiological investigations are needed to elucidate the role of the SNra in TLE. METHODS: Local field potentials (LFPs) and single-unit activities were simultaneously obtained from the basolateral amygdala (BLA) and the SNra in amygdala-kindled mice. The electrophysiological characteristics of the neuronal activities in the BLA and SNra were investigated. Directionality index was used to measure information flow between LFPs in the two areas during kindled seizures. The effects of electrical lesion of the SNra on the kindled seizures were analyzed in fully-kindled mice. RESULTS: The information flow was predominantly from the SNra to the BLA during the clonic-like periods of stage 5 seizures, but this phenomenon was not found during other kindled seizures. In fully-kindled mice, SNra lesions facilitated the kindled seizures. After lesions were inflicted, the afterdischarge durations and clonic-like periods of stage 5 seizures increased significantly. CONCLUSION: The electrophysiological and lesion results show that the SNra may play an anti-convulsant role in amygdala-kindled seizures.

Low-intensity ultrasound suppresses low-Mg2+-induced epileptiform discharges in juvenile mouse hippocampal slices.

OBJECTIVE: It has been shown that low-intensity ultrasound (LIUS) can suppress seizures in some laboratory studies. However, the mechanism of the suppression effect of LIUS remains unclear. The goal of this study is to investigate the modulation effects of focused LIUS on epileptiform discharges in mouse hippocampal slices as well as the underlying mechanism. APPROACH: Epileptiform discharges in hippocampal slices of 8 d-old mice were induced by low-Mg2+ artificial cerebrospinal fluid and recorded by a micro-electrode array in vitro. LIUS was delivered to hippocampal slices to investigate its modulation effects on epileptiform discharges. Pharmacological experiments were conducted to study the mechanism of the modulation effects. MAIN RESULTS: LIUS suppressed the amplitude, rate and duration of ictal discharges. For inter-ictal discharges, LIUS suppressed the amplitude but facilitated the rate. LIUS suppressed the spontaneous spiking activities of pyramidal neurons in CA3, and the suppression effect was eliminated by Kaliotoxin. The suppression effect of LIUS on epileptiform discharges was weakened when the perfusion was mixed with Kaliotoxin. SIGNIFICANCE: Those findings demonstrate that LIUS suppresses the epileptiform discharges in 8 d-old mouse hippocampal slices and that its suppression effect can mainly attributed to the activation of mechanosensitive Kv1.1 channels.

Phase Synchronization Dynamics of Neural Network during Seizures.

Epilepsy has been considered as a network-level disorder characterized by recurrent seizures, which result from network reorganization with evolution of synchronization. In this study, the brain networks were established by calculating phase synchronization based on electrocorticogram (ECoG) signals from eleven refractory epilepsy patients. Results showed that there was a significant increase of synchronization prior to seizure termination and no significant difference of the transitions of network states among the preseizure, seizure, and postseizure periods. Those results indicated that synchronization might participate in termination of seizures, and the network states transitions might not dominate the seizure evolution.

Two new chromene derivatives from Artemisia songarica.

Two new chromene derivatives, songaricachromenes A (1) and B (2), were isolated from Artemisia songarica, along with 10 known compounds (3-12). The structures and stereochemistry of the new compounds were elucidated by analyses of the NMR, MS, and electronic circular dichroism (ECD) data. All the isolates (1-12) were evaluated for their NO inhibitory effects on LPS-stimulated BV-2 microglial cells.

Automatic Delineation of the Clinical Target Volume in Rectal Cancer for Radiation Therapy using Three-dimensional Fully Convolutional Neural Networks.

Accurate, robust, and fast delineation of the clinical target volume (CTV) for the use in radiotherapy of rectal cancer (RC) is highly sought-after. Convolutional neural networks (CNNs) have proven themselves very effective in various segmentation tasks on medical images. Despite this, their application in CTV delineation is not yet fully explored. This study uses the three-dimensional fully convolutional neural network architecture called V-net for CTV delineation. The West China Hospital (Chengdu, China) provided this study with 120 annotated CT scans. For improved performance and to battle data scarcity, the available scans were augmented. Trained on 100 CT-scans for 20 hours and tested on 20 previously unseen CT-scans the network achieved a mean dice similarity coefficient (DSC) of 0.90 and a mean delineation time per CTV of 0.60 seconds. The proposed method is compared with two other state-of-the-art CNNs and is shown to be superior.

GABAA Receptor Activity Suppresses the Transition from Inter-ictal to Ictal Epileptiform Discharges in Juvenile Mouse Hippocampus.

Exploring the transition from inter-ictal to ictal epileptiform discharges (IDs) and how GABAA receptor-mediated action affects the onset of IDs will enrich our understanding of epileptogenesis and epilepsy treatment. We used Mg2+-free artificial cerebrospinal fluid (ACSF) to induce epileptiform discharges in juvenile mouse hippocampal slices and used a micro-electrode array to record the discharges. After the slices were exposed to Mg2+-free ACSF for 10 min-20 min, synchronous recurrent seizure-like events were recorded across the slices, and each event evolved from inter-ictal epileptiform discharges (IIDs) to pre-ictal epileptiform discharges (PIDs), and then to IDs. During the transition from IIDs to PIDs, the duration of discharges increased and the inter-discharge interval decreased. After adding 3 µmol/L of the GABAA receptor agonist muscimol, PIDs and IDs disappeared, and IIDs remained. Further, the application of 10 µmol/L muscimol abolished all the epileptiform discharges. When the GABAA receptor antagonist bicuculline was applied at 10 µmol/L, IIDs and PIDs disappeared, and IDs remained at decreased intervals. These results indicated that there are dynamic changes in the hippocampal network preceding the onset of IDs, and GABAA receptor activity suppresses the transition from IIDs to IDs in juvenile mouse hippocampus.

Nitric Oxide Inhibitory Sesquiterpenoids and Its Dimers from Artemisia freyniana.

Two new disesquiterpenoids (1 and 2) and 11 new (3-13) and 10 known (14-23) sesquiterpenoids were isolated from the whole plants of Artemisia freyniana. Their structures were elucidated by spectroscopic data analysis and comparison with published NMR data. The absolute configurations of the new isolates (1-13) were assigned based on single-crystal X-ray diffraction data and comparison of the experimental and calculated ECD data. The eremophilane derivatives 8 and 9 possess an unprecedented 2-isopropyl-3,7,7a-trimethyl-2,4,5,6,7,7a-hexahydro-1 H-indene scaffold, and a putative biosynthetic pathway for these compounds is proposed. Compounds 4, 5, and 9 exhibited inhibitory effects against LPS-stimulated nitric oxide (NO) production in RAW 264.7 macrophage cells with IC50 values of 10.8, 12.6, and 11.7 µM, respectively.

The Subiculum: A Potential Site of Ictogenesis in a Neonatal Seizure Model.

Studies have reported that the subiculum is one origin of interictal-like discharges in adult patients with temporal lobe epilepsy; however, whether the subiculum represents a site of ictogenesis for neonatal seizures remains unclear. In this study, multi-electrode recording techniques were used to record epileptiform discharges induced by low-Mg2+ or high-K+ artificial cerebrospinal fluid in neonatal mouse hippocampal slices, and the spatiotemporal dynamics of the epileptiform discharges were analyzed. The Na+-K+-2Cl- cotransporter 1 (NKCC1) blocker, bumetanide, was applied to test its effect upon epileptiform discharges in low-Mg2+ model. The effect of N-methyl-d-aspartate receptors (NMDARs) antagonist, d-AP5, upon the epileptiform discharges in high-K+ model was examined. We found that the neonatal subiculum not only relayed epileptiform discharges emanating from the hippocampus proper (HP) but also initiated epileptiform discharges (interictal- and ictal-like discharges) independently. The latency to onset of the first epileptiform discharge initiated in the subiculum was similar to that initiated in the HP. Bumetanide efficiently blocked seizures in the neonatal HP, but was less effectively in suppressing seizures initiated in the subiculum. In high-K+ model, d-AP5 was more effective in blocking seizures initiated in the subiculum than that initiated in the HP. Furthermore, Western blotting analysis showed that NKCC1 expression was lower in the subiculum than that in the HP, whereas the expression of NMDAR subunits, NR2A and NR2B, was higher in the subiculum than that in the HP. Our results revealed that the subiculum was a potential site of ictogenesis in neonatal seizures and possessed similar seizure susceptibility to the HP. GABAergic excitation resulting from NKCC1 may play a less dominant role during ictogenesis in the subiculum than that in the HP. The subicular ictogenesis may be related to the glutamatergic excitation mediated by NMDARs.

The oscillation-like activity in bullfrog ON-OFF retinal ganglion cell.

Oscillatory activity of retinal ganglion cell (RGC) has been observed in various species. It was reported such oscillatory activity is raised within large neural network and involved in retinal information coding. In the present research, we found an oscillation-like activity in ON-OFF RGC of bullfrog retina, and studied the mechanisms underlying the ON and OFF activities respectively. Pharmacological experiments revealed that the oscillation-like activity patterns in both ON and OFF pathways were abolished by GABA receptor antagonists, indicating GABAergic inhibition is essential for generating them. At the meantime, such activities in the ON and OFF pathways showed different responses to several other applied drugs. The oscillation-like pattern in the OFF pathway was abolished by glycine receptor antagonist or gap junction blocker, whereas that in the ON pathway was not affected. Furthermore, the blockade of the ON pathway by metabotropic glutamate receptor agonist led to suppression of the oscillation-like pattern in the OFF pathway. These results suggest that the ON pathway has modulatory effect on the oscillation-like activity in the OFF pathway. Therefore, the mechanisms underlying the oscillation-like activities in the ON and OFF pathways are different: the oscillation-like activity in the ON pathway is likely caused by GABAergic amacrine cell network, while that in the OFF pathway needs the contributions of GABAergic and glycinergic amacrine cell network, as well as gap junction connections.

Dynamic Network Connectivity Analysis to Identify Epileptogenic Zones Based on Stereo-Electroencephalography.

Objectives: Accurate localization of epileptogenic zones (EZs) is essential for successful surgical treatment of refractory focal epilepsy. The aim of the present study is to investigate whether a dynamic network connectivity analysis based on stereo-electroencephalography (SEEG) signals is effective in localizing EZs. Methods: SEEG data were recorded from seven patients who underwent presurgical evaluation for the treatment of refractory focal epilepsy and for whom the subsequent resective surgery gave a good outcome. A time-variant multivariate autoregressive model was constructed using a Kalman filter, and the time-variant partial directed coherence was computed. This was then used to construct a dynamic directed network model of the epileptic brain. Three graph measures (in-degree, out-degree, and betweenness centrality) were used to analyze the characteristics of the dynamic network and to find the important nodes in it. Results: In all seven patients, the indicative EZs localized by the in-degree and the betweenness centrality were highly consistent with the clinically diagnosed EZs. However, the out-degree did not indicate any significant differences between nodes in the network. Conclusions: In this work, a method based on ictal SEEG signals and effective connectivity analysis localized EZs accurately. The results suggest that the in-degree and betweenness centrality may be better network characteristics to localize EZs than the out-degree.

Interaction between Thalamus and Hippocampus in Termination of Amygdala-Kindled Seizures in Mice.

The thalamus and hippocampus have been found both involved in the initiation, propagation, and termination of temporal lobe epilepsy. However, the interaction of these regions during seizures is not clear. The present study is to explore whether some regular patterns exist in their interaction during the termination of seizures. Multichannel in vivo recording techniques were used to record the neural activities from the cornu ammonis 1 (CA1) of hippocampus and mediodorsal thalamus (MDT) in mice. The mice were kindled by electrically stimulating basolateral amygdala neurons, and Racine's rank standard was employed to classify the stage of behavioral responses (stage 1~5). The coupling index and directionality index were used to investigate the synchronization and information flow direction between CA1 and MDT. Two main results were found in this study. (1) High levels of synchronization between the thalamus and hippocampus were observed before the termination of seizures at stage 4~5 but after the termination of seizures at stage 1~2. (2) In the end of seizures at stage 4~5, the information tended to flow from MDT to CA1. Those results indicate that the synchronization and information flow direction between the thalamus and the hippocampus may participate in the termination of seizures.