Intolerance of uncertainty and attentional networks: Unique associations with alerting

The present study sought to extend our understanding as to how intolerance of uncertainty (IU) relates to information-processing by investigating associations between IU and attentional networks, including alerting, orienting, and executive attention. Based upon prior research, IU was expected to cluster with alerting. An unselected sample of college students (N = 86; 79% women) completed self-report measures of IU and state anxiety, as well as the attention network test. Among the attentional networks, IU only shared a positive association with alerting and the association remained intact after statistically controlling for state anxiety. State anxiety did not moderate the association between IU and alerting. Although two IU dimensions (prospective and inhibitory) both shared a positive association with alerting, only prospective IU was associated with alerting after statistically controlling for state anxiety. The results provide evidence that IU relates to an overfunctioning of the alerting attentional network, which may suggest a role of hypervigilance and a greater influence of bottom-up processing in relation to IU. Implications for how these results advance our understanding of possible links between IU and anxiety disorders are discussed.     

Structural architecture supports functional organization in the human aging brain at a regionwise and network level

Functional interactions in the brain are constrained by the underlying anatomical architecture, and structural and functional networks share network features such as modularity. Accordingly, age‐related changes of structural connectivity (SC) may be paralleled by changes in functional connectivity (FC). We provide a detailed qualitative and quantitative characterization of the SC–FC coupling in human aging as inferred from resting‐state blood oxygen‐level dependent functional magnetic resonance imaging and diffusion‐weighted imaging in a sample of 47 adults with an age range of 18–82. We revealed that SC and FC decrease with age across most parts of the brain and there is a distinct age‐dependency of regionwise SC–FC coupling and network‐level SC–FC relations. A specific pattern of SC–FC coupling predicts age more reliably than does regionwise SC or FC alone (r = 0.73, 95% CI = [0.7093, 0.8522]). Hence, our data propose that regionwise SC–FC coupling can be used to characterize brain changes in aging. Hum Brain Mapp 37:2645–2661, 2016. © 2016 Wiley Periodicals, Inc.     

Functional connectivity and information flow of the respiratory neural network in chronic obstructive pulmonary disease

Breathing involves a complex interplay between the brainstem automatic network and cortical voluntary command. How these brain regions communicate at rest or during inspiratory loading is unknown. This issue is crucial for several reasons: (i) increased respiratory loading is a major feature of several respiratory diseases, (ii) failure of the voluntary motor and cortical sensory processing drives is among the mechanisms that precede acute respiratory failure, (iii) several cerebral structures involved in responding to inspiratory loading participate in the perception of dyspnea, a distressing symptom in many disease. We studied functional connectivity and Granger causality of the respiratory network in controls and patients with chronic obstructive pulmonary disease (COPD), at rest and during inspiratory loading. Compared with those of controls, the motor cortex area of patients exhibited decreased connectivity with their contralateral counterparts and no connectivity with the brainstem. In the patients, the information flow was reversed at rest with the source of the network shifted from the medulla towards the motor cortex. During inspiratory loading, the system was overwhelmed and the motor cortex became the sink of the network. This major finding may help to understand why some patients with COPD are prone to acute respiratory failure. Network connectivity and causality were related to lung function and illness severity. We validated our connectivity and causality results with a mathematical model of neural network. Our findings suggest a new therapeutic strategy involving the modulation of brain activity to increase motor cortex functional connectivity and improve respiratory muscles performance in patients. Hum Brain Mapp 37:2736–2754, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.     

Spatial patterns of atrophy,hypometabolism, and amyloid deposition in Alzheimer's disease correspond to dissociable functional brain networks

Recent neuroimaging studies of Alzheimer's disease (AD) have emphasized topographical similarities between AD‐related brain changes and a prominent cortical association network called the default‐mode network (DMN). However, the specificity of distinct imaging abnormalities for the DMN compared to other intrinsic connectivity networks (ICNs) of the limbic and heteromodal association cortex has not yet been examined systematically. We assessed regional amyloid load using AV45‐PET, neuronal metabolism using FDG‐PET, and gray matter volume using structural MRI in 473 participants from the Alzheimer's Disease Neuroimaging Initiative, including preclinical, predementia, and clinically manifest AD stages. Complementary region‐of‐interest and voxel‐based analyses were used to assess disease stage‐ and modality‐specific changes within seven principle ICNs of the human brain as defined by a standardized functional connectivity atlas. Amyloid deposition in AD dementia showed a preference for the DMN, but high effect sizes were also observed for other neocortical ICNs, most notably the frontoparietal‐control network. Atrophic changes were most specific for an anterior limbic network, followed by the DMN, whereas other neocortical networks were relatively spared. Hypometabolism appeared to be a mixture of both amyloid‐ and atrophy‐related profiles. Similar patterns of modality‐dependent network specificity were also observed in the predementia and, for amyloid deposition, in the preclinical stage. These quantitative data confirm a high vulnerability of the DMN for multimodal imaging abnormalities in AD. However, rather than being selective for the DMN, imaging abnormalities more generally affect higher order cognitive networks and, importantly, the vulnerability profiles of these networks markedly differ for distinct aspects of AD pathology. Hum Brain Mapp 37:35–53, 2016. © 2015 Wiley Periodicals, Inc.     

Altered Integration of Structural Covariance Networks in Young Children With Type 1 Diabetes

Type 1 diabetes mellitus (T1D), one of the most frequent chronic diseases in children, is associated with glucose dysregulation that contributes to an increased risk for neurocognitive deficits. While there is a bulk of evidence regarding neurocognitive deficits in adults with T1D, little is known about how early‐onset T1D affects neural networks in young children. Recent data demonstrated widespread alterations in regional gray matter and white matter associated with T1D in young children. These widespread neuroanatomical changes might impact the organization of large‐scale brain networks. In the present study, we applied graph‐theoretical analysis to test whether the organization of structural covariance networks in the brain for a cohort of young children with T1D (N = 141) is altered compared to healthy controls (HC; N = 69). While the networks in both groups followed a small world organization—an architecture that is simultaneously highly segregated and integrated—the T1D network showed significantly longer path length compared with HC, suggesting reduced global integration of brain networks in young children with T1D. In addition, network robustness analysis revealed that the T1D network model showed more vulnerability to neural insult compared with HC. These results suggest that early‐onset T1D negatively impacts the global organization of structural covariance networks and influences the trajectory of brain development in childhood. This is the first study to examine structural covariance networks in young children with T1D. Improving glycemic control for young children with T1D might help prevent alterations in brain networks in this population. Hum Brain Mapp 37:4034–4046, 2016. © 2016 Wiley Periodicals, Inc .     

Negative functional coupling between the right fronto-parietal and limbic resting state networks predicts increased self-control and later substance use onset in adolescence

Recent developmental brain imaging studies have demonstrated that negatively coupled prefrontal-limbic circuitry implicates the maturation of brain development in adolescents. Using resting-state functional magnetic resonance imaging (rs-fMRI) and independent component analysis (ICA), the present study examined functional network coupling between prefrontal and limbic systems and links to self-control and substance use onset in adolescents. Results suggest that negative network coupling (anti-correlated temporal dynamics) between the right fronto-parietal and limbic resting state networks is associated with greater self-control and later substance use onset in adolescents. These findings increase our understanding of the developmental importance of prefrontal-limbic circuitry for adolescent substance use at the resting-state network level.     

痹康饮合剂对胶原性关节炎大鼠IL-1β和TNF—α的影响

目的 观察痹康饮合剂对胶原性关节炎的治疗作用，探讨痹康饮合剂（Bikangyin Mixture，BKY）治疗类风湿性关节炎（RA）的可能免疫作用机制。方法 牛源性CII皮下注射诱导建立胶原性关节炎模型随机分为正常组、痹康饮高剂量组、痹康饮低剂量组、甲氨喋呤（Methotrexat，MTX）组、模型对照组。观察比较各组大鼠细胞因子IL-1β和TNF—α水平的变化。结果 痹康饮合剂能显著降低CIA大鼠血清异常增高的IL-1β、TNF—α水平，其治疗作用与甲氨喋呤大致相同，但不良反应较甲氨喋呤小。结论 痹康饮合剂治疗CIA大鼠的作用机制可能与下调致炎细胞因子IL-1β和TNF—α有关。     

微创经皮肾穿刺输尿管镜取石术的配合及护理

目的探讨经皮肾穿刺输尿管镜取石术的有效配合及护理要点。方法分析236例微创经皮肾穿刺输尿管镜取石术的配合及护理经验。结果236例患者手术均进行顺利,患者无意外事故及并发症发生。结论有效的配合及护理,是保证手术的顺利进行,避免术中、术后并发症发生,使患者更快恢复健康的关键。     

基于3D-ResNet深度影像特征的胆囊癌生存预测模型

建立一个精准的个体化胆囊癌患者生存预测模型,分析、寻找新的胆囊癌预后因素,对于患者预后评估、治疗模式选择、手术患者筛选、术后辅助治疗方案确定及医疗资源合理使用均具有重要意义。本文提出一种基于3D-ResNet提取深度影像特征建立胆囊癌患者生存预后模型的方法,通过迁移学习以及训练3D-ResNet自动提取患者CT的深度特征,并利用提取的深度影像特征,通过Cox比例风险回归模型建立胆囊癌患者的生存预测模型。实验结果表明,基于深度影像特征建立的胆囊癌患者预后因子在预测患者生存时的C指数达到0.734,利用深度影像特征预后因子预测患者的1、3、5年存活率AUC分别达到0.833、0.791、0.813。本方法对胆囊癌预后预测有着良好的指示作用。     

基于3D深度残差网络和多模态MRI的脑胶质瘤自动分级

目的:利用3D深度残差网络和多模态MRI实现对脑胶质瘤的自动分级。方法:利用BraTS2020公共数据集的293例高级别胶质瘤（HGG）和76例低级别胶质瘤（LGG）的多模态MRI数据训练和测试3D深度残差卷积网络模型。多模态MRI图像经过3D剪裁、重采样和归一化的预处理,随机分组为训练（64%）、验证（16%）和测试（20%）样本,将预处理后的多模态MRI图像和分级标注输入到网络模型进行训练、验证和测试。利用准确率（ACC）和受试者工作特征（ROC）曲线下面积（AUC）评价分级结果。结果:在59例（48例HGG和11例LGG）验证数据集上,ACC和AUC分别为0.93和0.97,在75例（62例HGG和13例LGG）测试数据集上,ACC和AUC分别为0.89和0.93。结论:3D深度残差网络在多模态MRI数据集上获得了较好的脑胶质瘤自动分级结果,可以为确定治疗方案和预测预后方面提供重要参考。