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.

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.

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.

[Information coding in retinal ganglion cells].

In vertebrate visual system, retina is the first stage for visual information processing. Retinal ganglion cells are the only output neurons of the retina, and their firing activities are dependent on visual stimuli. Retinal ganglion cells can effectively encode visual information via various manners, such as firing rate, temporal structure of spike trains, and concerted activity, etc. Adaptation is one of the basic characteristics of the nervous system, which enables retinal neurons to encode stimuli under a wide variety of natural conditions with limited range in their output. This article reviews the recent studies focused on the coding properties and adaptation of retinal ganglion cells. Relevant issues about dynamical adjustment of coding strategies of retinal ganglion cells in response to different visual stimulation, as well as physiological property and function of adaptation are discussed.

Response dynamics of bullfrog ON-OFF RGCs to different stimulus durations.

Stimulus duration is an important feature of visual stimulation. In the present study, response properties of bullfrog ON-OFF retinal ganglion cells (RGCs) in exposure to different visual stimulus durations were studied. By using a multi-electrode recording system, spike discharges from ON-OFF RGCs were simultaneously recorded, and the cells' ON and OFF responses were analyzed. It was found that the ON response characteristics, including response latency, spike count, as well as correlated activity and relative latency between pair-wise cells, were modulated by different light OFF intervals, while the OFF response characteristics were modulated by different light ON durations. Stimulus information carried by the ON and OFF responses was then analyzed, and it was found that information about different light ON durations was more carried by transient OFF response, whereas information about different light OFF intervals were more carried by transient ON response. Meanwhile, more than 80 % information about stimulus durations was carried by firing rate. These results suggest that ON-OFF RGCs are sensitive to different stimulus durations, and they can efficiently encode the information about visual stimulus duration by firing rate.

Gap junction permeability modulated by dopamine exerts effects on spatial and temporal correlation of retinal ganglion cells' firing activities.

Synchronized activities among retinal ganglion cells (RGCs) via gap junctions can be increased by exogenous dopamine (DA). During DA application, single neurons' firing activities become more synchronized with its adjacent neighbors. One intriguing question is how the enhanced spatial synchronization alters the temporal firing structure of single neurons. In the present study, firing activities of bullfrog's dimming detectors in response to binary pseudo-random checker-board flickering were recorded via a multi-channel recording system. DA was applied in the retina to modulate synchronized activities between RGCs, and the effect of DA on firing activities of single neurons was examined. It was found that, during application of DA, synchronized activities between single neuron and its neighboring neurons was enhanced. At the meantime, the temporal structures of single neuron spike train changed significantly, and the temporal correlation in single neuron's response decreased. The pharmacological study results indicated that the activation of D1 receptor might have effects on gap junction permeability between RGCs. Our results suggested that the dopaminergic pathway participated in the modulation of spatial and temporal correlation of RGCs' firing activities, and may exert critical effects on visual information processing in the retina.

The role of the entorhinal cortex in epileptiform activities of the hippocampus.

BACKGROUND: Temporal lobe epilepsy (TLE) is the commonest type of epilepsy in adults, and the hippocampus is indicated to have a close relationship with TLE. Recent researches also indicate that the entorhinal cortex (EC) is involved in epilepsy. To explore the essential role that the EC may play in epilepsy, a computational model of the hippocampal CA3 region was built, which consisted of pyramidal cells and two types of interneurons. By changing the input signals from the EC, the effects of EC on epileptiform activities of the hippocampus were investigated. Additionally, recent studies have found that the antiepileptic drug valproate (VPA) can block ictal discharges but cannot block interictal discharges in vitro, and the mechanism under this phenomenon is still confusing. In our model, the effects of VPA on epileptiform activities were simulated and some mechanisms were explored. RESULTS: Interictal discharges were induced in the model without the input signals from the EC, whereas the model with the EC input produced ictal discharges when the EC input contained ictal discharges. The GABA-ergic connection strength was enhanced and the NMDA-ergic connection strength was reduced to simulate the effects of VPA, and the simulation results showed that the disappearance of ictal discharges in the model mainly due to the disappearance of ictal discharges in the input signals from the EC. CONCLUSIONS: Simulation results showed that ictal discharges in the EC were necessary for the hippocampus to generate ictal discharges, and VPA might block the ictal discharges in the EC, which led to the disappearance of ictal discharges in the hippocampus.

Effect of the entorhinal cortex on ictal discharges in low-Mg²âº-induced epileptic hippocampal slice models.

The hippocampus plays an important role in the genesis of mesial temporal lobe epilepsy, and the entorhinal cortex (EC) may affect the hippocampal network activity because of the heavy interconnection between them. However, the mechanism by which the EC affects the discharge patterns and the transmission mode of epileptiform discharges within the hippocampus needs further study. Here, multielectrode recording techniques were used to study the spatiotemporal characteristics of epileptiform discharges in adult mouse hippocampal slices and combined EC-hippocampal slices and determine whether and how the EC affects the hippocampal neuron discharge patterns. The results showed that low-Mg²âº artificial cerebrospinal fluid induced interictal discharges in hippocampal slices, whereas, in combined EC-hippocampal slices the discharge pattern was alternated between interictal and ictal discharges, and ictal discharges initiated in the EC and propagated to the hippocampus. The pharmacological effect of the antiepileptic drug valproate (VPA) was tested. VPA reversibly suppressed the frequency of interictal discharges but did not change the initiation site and propagation speed, and it completely blocked ictal discharges. Our results suggested that EC was necessary for the hippocampal ictal discharges, and ictal discharges were more sensitive than interictal discharges in response to VPA.

Involvement of thalamus in initiation of epileptic seizures induced by pilocarpine in mice.

Studies have suggested that thalamus is involved in temporal lobe epilepsy, but the role of thalamus is still unclear. We obtained local filed potentials (LFPs) and single-unit activities from CA1 of hippocampus and parafascicular nucleus of thalamus during the development of epileptic seizures induced by pilocarpine in mice. Two measures, redundancy and directionality index, were used to analyze the electrophysiological characters of neuronal activities and the information flow between thalamus and hippocampus. We found that LFPs became more regular during the seizure in both hippocampus and thalamus, and in some cases LFPs showed a transient disorder at seizure onset. The variation tendency of the peak values of cross-correlation function between neurons matched the variation tendency of the redundancy of LFPs. The information tended to flow from thalamus to hippocampus during seizure initiation period no matter what the information flow direction was before the seizure. In some cases the information flow was symmetrically bidirectional, but none was found in which the information flowed from hippocampus to thalamus during the seizure initiation period. In addition, inactivation of thalamus by tetrodotoxin (TTX) resulted in a suppression of seizures. These results suggest that thalamus may play an important role in the initiation of epileptic seizures.

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.

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.

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.

Multi-channel neural data analysis methods and applications.

Multi-channel recording is now a widely used tool in neuroscience research that makes it possible to study the activity of neuron population. The data collected from multiple neurons indicate important information of neural coding rules and brain functional mechanism, which requires advanced computational techniques to decode. Here we reviewed the methods for multi-channel neural data analysis and gave brief introduction of their typical application for studying different kinds of neural data.

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.

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.

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.

[Exploring spatiotemporal patterns of epileptiform discharge in hippocampal slice using multi-electrode arrays].

To investigate the spatiotemporal properties of epileptiform activity in vitro, 400 microm-thick transverse hippocampal slices were prepared from juvenile rat and planar multi-electrode array (MEA) containing 60 electrodes was used to record the electrical activity induced by bath application of high potassium artificial cerebrospinal fluid (ACSF) on slices. Following successful induction of epileptiform bursts, phenobarbital sodium was applied to test for its inhibitory effects on bursting activity in different regions of slice. Region-specific characteristics of epileptiform activity and anticonvulsant actions of phenobarbital sodium in the hippocampal network were determined by comparing the population activity obtained from MEA. The results showed that: (1) 15 min after high-K+ ACSF application, rhythmic and synchronous epileptiform bursts could be detected from all CA sub-regions. Quantitative analysis indicates that the firing patterns of different CA sub-regions were not statistically different (P>0.05). However, no bursting activity was recorded from granular cells in dentate gyrus, only sparse spikes were observed, with frequency significantly lower than that in CA regions (P<0.05). (2) The high-K+-induced bursting activity could last for more than 40 min with stable bursting activities. (3) Bath application of 60 micromol/L phenobarbital sodium inhibited the bursting activities on hippocampal slice. Bursting activities in CA3c and CA1 were firstly suppressed. 10 min after the phenobarbital sodium application, strong bursting activities persisted only in some of pyramidal cells in CA3a and CA3b. These results show that MEA could be applied for studying the spatial and temporal properties of epileptiform activity in vitro, as well as the region-specific effects of anti-epileptic drugs.

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.