Epilepsy and pregnancy: What should the neurologists do?

Epilepsy is one of the most common chronic disorders affecting women of childbearing age. Unfortunately, many women with epilepsy (WWE) still report not receiving key information about pregnancy. They obviously need information about epilepsy and pregnancy prior to conception with a particular emphasis on effective birth control (i.e. contraception), necessity to plan pregnancy, antiepileptic drugs optimization, and folate supplementation. The risks associated with use of antiepileptic drugs during pregnancy have to be balanced against fetal and maternal risks associated with uncontrolled seizures. This report reviews evidence-based counseling and management strategies concerning maternal and fetal risks associated with seizures, teratogenic risks associated with antiepileptic drug exposure with a special emphasis on developmental and behavioural outcomes of children exposed to intra utero antiepileptic drugs.     

Differentiating ictal/subclinical spikes and waves in childhood absence epilepsy by spectral and network analyses: A pilot study

ObjectiveChildhood absence epilepsy (CAE) is a disease with distinct seizure semiology and electroencephalographic (EEG) features. Differentiating ictal and subclinical generalized spikes and waves discharges (GSWDs) in the EEG is challenging, since they appear to be identical upon visual inspection. Here, spectral and functional connectivity (FC) analyses were applied to routine EEG data of CAE patients, to differentiate ictal and subclinical GSWDs.MethodsTwelve CAE patients with both ictal and subclinical GSWDs were retrospectively selected for this study. The selected EEG epochs were subjected to frequency analysis in the range of 1–30 Hz. Further, FC analysis based on the imaginary part of coherency was used to determine sensor level networks.ResultsDelta, alpha and beta band frequencies during ictal GSWDs showed significantly higher power compared to subclinical GSWDs. FC showed significant network differences for all frequency bands, demonstrating weaker connectivity between channels during ictal GSWDs.ConclusionUsing spectral and FC analyses significant differences between ictal and subclinical GSWDs in CAE patients were detected, suggesting that these features could be used for machine learning classification purposes to improve EEG monitoring.SignificanceIdentifying differences between ictal and subclinical GSWDs using routine EEG, may improve understanding of this syndrome and the management of patients with CAE.     

基于网络药理学和分子对接法探索复方一枝蒿颗粒治疗新型冠状病毒肺炎的潜在分子机制

目的 通过网络药理学及分子对接技术探寻复方一枝蒿颗粒治疗新型冠状病毒肺炎的作用机制。方法 应用TCMSP数据库、BATMAN-TCM数据库及文献收集复方一枝蒿颗粒活性成分及潜在靶点。通过TTD、GeneCards和OMMI数据库检索新型冠状病毒肺炎相关的靶点。将复方一枝蒿颗粒药物靶点和新型冠状病毒肺炎相关基因取交集，使用String数据库构建靶蛋白相互作用（PPI）网络。通过Cytoscape构建“药物–活性成分–靶点基因–疾病”网络。对交集靶点进行GO功能、KEGG通路富集分析。利用Autodock_vina软件对活性成分和靶点进行分子对接。结果 共筛选92个活性成分，1 627个靶点，新型冠状病毒肺炎疾病靶点464个，两者取交集筛选出87个潜在靶点。GO功能富集得到2 040个条目（P＜0.05），与病毒过程、参与共生相互作用的生物过程、活性氧代谢过程的调节、与宿主相互作用的生物过程、病毒生命周期、炎症反应的调节等生物学过程有关。KEGG通路分析共得到150条通路，与新冠肺炎密切相关的有人巨细胞病毒感染、结核、COVID-19、IL-17信号通路等。分子对接结果证实，筛选的靶点受体蛋白与活性成分可以较好地结合。结论 复方一枝蒿颗粒可通过多组分、多靶点和多途径的方式对新型冠状病毒肺炎产生治疗作用。     

Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach

PurposeTo show that a deep learning (DL)–based, automated model for Lipiodol (Guerbet Pharmaceuticals, Paris, France) segmentation on cone-beam computed tomography (CT) after conventional transarterial chemoembolization performs closer to the “ground truth segmentation” than a conventional thresholding-based model.Materials and MethodsThis post hoc analysis included 36 patients with a diagnosis of hepatocellular carcinoma or other solid liver tumors who underwent conventional transarterial chemoembolization with an intraprocedural cone-beam CT. Semiautomatic segmentation of Lipiodol was obtained. Subsequently, a convolutional U-net model was used to output a binary mask that predicted Lipiodol deposition. A threshold value of signal intensity on cone-beam CT was used to obtain a Lipiodol mask for comparison. The dice similarity coefficient (DSC), mean squared error (MSE), center of mass (CM), and fractional volume ratios for both masks were obtained by comparing them to the ground truth (radiologist-segmented Lipiodol deposits) to obtain accuracy metrics for the 2 masks. These results were used to compare the model versus the threshold technique.ResultsFor all metrics, the U-net outperformed the threshold technique: DSC (0.65 ± 0.17 vs 0.45 ± 0.22, P < .001) and MSE (125.53 ± 107.36 vs 185.98 ± 93.82, P = .005). The difference between the CM predicted and the actual CM was 15.31 mm ± 14.63 versus 31.34 mm ± 30.24 (P < .001), with lesser distance indicating higher accuracy. The fraction of volume present ([predicted Lipiodol volume]/[ground truth Lipiodol volume]) was 1.22 ± 0.84 versus 2.58 ± 3.52 (P = .048) for the current model’s prediction and threshold technique, respectively.ConclusionsThis study showed that a DL framework could detect Lipiodol in cone-beam CT imaging and was capable of outperforming the conventionally used thresholding technique over several metrics. Further optimization will allow for more accurate, quantitative predictions of Lipiodol depositions intraprocedurally.     

孤独症谱系障碍社会脑神经网络基础及其研究进展

孤独症谱系障碍(ASD)的核心诊断特征是社会交流障碍以及受限和重复的行为和兴趣,症状常在生命的最初几年出现。过去的二十年内,神经成像揭示了许多“社会性”大脑的非典型活动和异常连通性的发现,包括梭状回对面部和凝视的分析、杏仁核的情绪处理、默认模式网络的心智化以及镜像神经元相关区域对他人行为的模仿和理解,但对于社交功能缺陷潜在的神经生理机制还未达成一致。除了方法学上的挑战以及聚焦于单个大脑网络的连通性研究以外,更深层次的问题是ASD的极强异质性。这种不一致的发现,可能是由于诊断标准的变化以及学龄前期的ASD患儿存在非典型的神经发育轨迹,因此,有必要进行孤独症亚型的队列研究和大样本的纵向队列研究。     

Frequency-specific effects of low-intensity rTMS can persist for up to 2 weeks post-stimulation: A longitudinal rs-fMRI/MRS study in rats

BackgroundEvidence suggests that repetitive transcranial magnetic stimulation (rTMS), a non-invasive neuromodulation technique, alters resting brain activity. Despite anecdotal evidence that rTMS effects wear off, there are no reports of longitudinal studies, even in humans, mapping the therapeutic duration of rTMS effects.ObjectiveHere, we investigated the longitudinal effects of repeated low-intensity rTMS (LI-rTMS) on healthy rodent resting-state networks (RSNs) using resting-state functional MRI (rs-fMRI) and on sensorimotor cortical neurometabolite levels using proton magnetic resonance spectroscopy (MRS).MethodsSprague-Dawley rats received 10 min LI-rTMS daily for 15 days (10 Hz or 1 Hz stimulation, n = 9 per group). MRI data were acquired at baseline, after seven days and after 14 days of daily stimulation and at two more timepoints up to three weeks post-cessation of daily stimulation.Results10 Hz stimulation increased RSN connectivity and GABA, glutamine, and glutamate levels. 1 Hz stimulation had opposite but subtler effects, resulting in decreased RSN connectivity and glutamine levels. The induced changes decreased to baseline levels within seven days following stimulation cessation in the 10 Hz group but were sustained for at least 14 days in the 1 Hz group.ConclusionOverall, our study provides evidence of long-term frequency-specific effects of LI-rTMS. Additionally, the transient connectivity changes following 10 Hz stimulation suggest that current treatment protocols involving this frequency may require ongoing “top-up” stimulation sessions to maintain therapeutic effects.     

Vulnerability of multiple large‐scale brain networks in dementia with Lewy bodies

Aberrations of large‐scale brain networks are found in the majority of neurodegenerative disorders. The brain connectivity alterations underlying dementia with Lewy bodies (DLB) remain, however, still elusive, with contrasting results possibly due to the pathological and clinical heterogeneity characterizing this disorder. Here, we provide a molecular assessment of brain network alterations, based on cerebral metabolic measurements as proxies of synaptic activity and density, in a large cohort of DLB patients (N = 72). We applied a seed‐based interregional correlation analysis approach (p < .01, false discovery rate corrected) to evaluate large‐scale resting‐state networks' integrity and their interactions. We found both local and long‐distance metabolic connectivity alterations, affecting the posterior cortical networks, that is, primary visual and the posterior default mode network, as well as the limbic and attention networks, suggesting a widespread derangement of the brain connectome. Notably, patients with the lowest visual and attention cognitive scores showed the most severe connectivity derangement in regions of the primary visual network. In addition, network‐level alterations were differentially associated with the core clinical manifestations, namely, hallucinations with more severe metabolic dysfunction of the attention and visual networks, and rapid eye movement sleep behavior disorder with alterations of connectivity of attention and subcortical networks. These multiple network‐level vulnerabilities may modulate the core clinical and cognitive features of DLB and suggest that DLB should be considered as a complex multinetwork disorder.     

Evaluating alcohol and marijuana use among emerging adult males via analysis of text messages

Background: Advances in social technologies offer new tools for large scale data collection and analysis of peer influence and social networks on substance use attitudes and behaviors. Objective: The objective of this study was to determine if text message content can predict alcohol and marijuana use attitudes and behaviors. Methods: Text messages from 91 males ages 18–25 were monitored over a period of 6 months and examined for content related to alcohol and marijuana. Self-report data indicating alcohol and marijuana use attitudes and behaviors were used to determine relationships between text message content, social network structure, and substance use attitudes and behaviors. Results: In total, 23,173 text messages were analyzed with 166 text messages including alcohol related terms and 195 text messages including drug related terms. Individuals who sent text messages related to alcohol use were more likely to have problem alcohol use and positive attitudes toward alcohol use, and individuals who sent text messages related to marijuana use reported higher frequency of marijuana use and more positive attitudes toward marijuana use. Individuals with multiple daily marijuana use were in positions that had less control over network structure. Conclusions: The results of this study indicate that monitoring text message content and social network structure among emerging adult males can potentially predict alcohol and marijuana use attitudes and behaviors. Text message content analysis is a novel technique increasing our understanding of the role of peer influence and social network on substance use attitudes and behaviors.