基于数据类型的症状网络分类：同期网络动态网络和个体化网络

现有研究涉及较多的症状网络包括同期网络、动态网络和时态/个体化网络。同期网络是基于同一时间点的横断面症状数据所构建的网络，动态网络则关注症状随时间的变化，而时序/个体化网络则聚焦于个体症状的相似性和关联度。本文重点介绍3种症状网络的定义、数据类型、网络模型、分析方法及应用，以便护理研究者更全面且清晰地了解症状网络。     

青少年网络犯罪的心理因素及其预防

目的：分析青少年网络犯罪的特点和成因，制定积极有效的防范对策和措施，对于青少年的健康成长和社会的安定具有十分重要的现实意义。资料来源；应用网络www．google.com，用“青少年网络犯罪”检索近几年相关文章，以及万方数据库中关于网络犯罪的相关文献，检索词：网络犯罪(network crime)、网络道德规范(network moral crlterlons)、网络心理健康教育(network psvchological heahh education)。资料选择：选择网络犯罪、青少年网络犯罪、网络道德规范、网络心理健康教育、青少年犯罪等相关文献36篇。资料提炼：在36篇文献中，内容呈不同程度重复的有24篇，给予删除；对12篇文献进行分类整理，用于综述，其中4篇选用为参考文献。资料综合：青少年网络犯罪的特点主要有犯罪主体的低龄化、多元化及犯罪的虚幻性这几个方面，青少年网络犯罪的成因与不良的网络信息对青少年身心的污染及青少年自身的心理因素有关，应加强青少年网络心理健康教育，净化青少年的心理环境，构建正确的网络道德规范，加强网络道德宣传和教育。结论：社会、学校和家长应该联合起来，加强网络道德和心理健康的宣传和教育，引导青少年构建正确的网络道德规范，净化青少年的心理环境，从根源上消除青少年犯罪的动机和心理诱因，以减少青少年网络犯罪的发生，维护青少年的心身健康。     

青少年网络犯罪的心理因素及其预防

目的:分析青少年网络犯罪的特点和成因,制定积极有效的防范对策和措施,对于青少年的健康成长和社会的安定具有十分重要的现实意义。资料来源:应用网络www.google.com,用“青少年网络犯罪”检索近几年相关文章,以及万方数据库中关于网络犯罪的相关文献,检索词:网络犯罪(networkcrime)、网络道德规范(networkmoralcriterions)、网络心理健康教育(networkpsychologicalhealtheducation)。资料选择:选择网络犯罪、青少年网络犯罪、网络道德规范、网络心理健康教育、青少年犯罪等相关文献36篇。资料提炼:在36篇文献中,内容呈不同程度重复的有24篇,给予删除;对12篇文献进行分类整理,用于综述,其中4篇选用为参考文献。资料综合:青少年网络犯罪的特点主要有犯罪主体的低龄化、多元化及犯罪的虚幻性这几个方面,青少年网络犯罪的成因与不良的网络信息对青少年身心的污染及青少年自身的心理因素有关,应加强青少年网络心理健康教育,净化青少年的心理环境,构建正确的网络道德规范,加强网络道德宣传和教育。结论:社会、学校和家长应该联合起来,加强网络道德和心理健康的宣传和教育,引导青少年构建正确的网络道德规范,净化青少年的心理环境,从根源上消除青少年犯罪的动机和心理诱因,以减少青少年网络犯罪的发生,维护青     

当代大学生网络交往行为分析

了解大学生网络交往行为的基本情况及交往方式、交往状况,检出大学生网络成瘾率。笔者对291名大学生进行调查,使用自编的大学生基本情况表、网络交往情况调查表和网络成瘾测验,其大学生网络成瘾率为5.2%。     

谈信息时代的学校网络教学

现在地时代是信息时代，从网络及网络教学的特点出发。分析了学校开展网络教学的重要意义，提出了目前开展网络教学的迫切需要关注的几个问题。     

浅谈信息时代的学校网络教学

本文从信息时代对人才素质结构的要求、网络及网络教学的特点出发，分析了学校开展网络教学的重要意义；分析了网络教学的开展给学校教学带来的新变化、新特点；最后提出了目前开展网络教学的需要关注的几个问题。     

网络信息自组织下的信息安全问题探讨

本文从生命节律的自组织出发,分析得出网络信息的自组织特性,并结合目前制约网络信息发展一个重要课题--网络信息安全,提出了自己的一些新的见解.     

网络成瘾症的理论模型、测量工具及其心理干预

目的：近年来对网络成瘾现象的研究主要集中在对该现象的定性描述和理论探讨上，其研究成果主要集中在网络普及最早、最广的美国，研究方法多以在线调查方式进行。国内对此研究起步稍晚，对网络心理学尚缺乏系统的研究，有关此方面的认识大多只是陈述网络成瘾的现象及对策，或单一的从一个方面（如：性别，性格等）进行的探索，缺少对网络成瘾行为背后的心理机制和行为动机的理论研究和实验论证。资料来源：以网络成瘾（internet addiction）、病态网络使用（pathological internet use）为检索词，全面检索www．googie．com和中国期刊全文数据库（1994-2004），获取关于网络成瘾研究方面的所有相关文献。资料选择：对获取的资料筛选出综述主题文献，并对重点文献的主要参考文献进行检索追踪相关全文。资料提炼：共收集到29篇符合标准的相关文献，对其进行分类整理，其中11篇选用为参考文献。资料综合：近年来对网络成瘾现象的研究主要集中在对该现象的定性描述和理论探讨上，缺少对网络成瘾行为背后的心理机制和行为动机的理论研究和实验论证。结论：网络成瘾是一种心理上的对网络的依赖，需要从心理层面进行探讨。研究者应当从网络本身的特点及被试者自身的心理特征以及社会因素出发，对网络成瘾这一概念进行科学、准确的界定。基于这一界定，根据心理测量学的原则，编制出严格科学的测量工具。研究者还应当综合运用调查研究、个案研究和实验研究等多种研究方法，对网络成瘾的特点及形成机制进行深入全面的探讨。     

城市高中生网络成瘾状况与相关因素

目的:了解城市高中生网络成瘾现状,并分析其相关因素。方法:调查于2005-09/11完成。对郑州市6所高中高一至高三年级学生按年级进行整群随机抽样,选取850人为调查对象,获得有效答卷806份,有效应答率为94.82%。所有调查对象均自愿参加调查。采用网络成瘾诊断问卷(问卷由8个问题组成,以“是”与“否”作答,是为1分,否为0分,≥5分即可判定为网络成瘾者)和作者编制的网络使用调查问卷(项目包括学校性质、性别、年级、生源、经常上网地点、每天平均上网时间、网龄、上网活动、上网体验等内容)对所有调查对象进行测试。结果:进入结果分析806人,脱落44人中21人从未使用网络,其余23人均未能完整回答调查问卷。①城市高中生网络成瘾总发生率为6.10%。②职业高中学生的网络成瘾发生率显著高于重点及普通高学生(χ2=9.556,P=0.008)。性别和学校性质在网络成瘾倾向上存在主效应(F=5.193,8.979,P=0.023,0.000)。性别和年级、年级和学校性质都对网络成瘾总分有显著的交互作用(P=0.029,0.007)。③网络成瘾与否与网龄、每天上网时间、上网活动均存在显著相关性(r=0.137,0.108,0.139,P=0.000,0.002,0.000)。④网络成瘾者与未成瘾者的网龄、每天上网时间长度、上网活动情况差异均有显著性意义(χ2=16.171,13.242,37.174,P=0.001,0.01,0.000)。结论:城市高中生网络成瘾者有其自身特点,需要采取有针对性的有效措施,维护其身心健康。     

网络成瘾症的理论模型、测量工具及其心理干预

目的:近年来对网络成瘾现象的研究主要集中在对该现象的定性描述和理论探讨上,其研究成果主要集中在网络普及最早、最广的美国,研究方法多以在线调查方式进行。国内对此研究起步稍晚,对网络心理学尚缺乏系统的研究,有关此方面的认识大多只是陈述网络成瘾的现象及对策,或单一的从一个方面(如:性别,性格等)进行的探索,缺少对网络成瘾行为背后的心理机制和行为动机的理论研究和实验论证。资料来源:以网络成瘾(internetaddiction)、病态网络使用(pathologicalinternetuse)为检索词,全面检索www.google.com和中国期刊全文数据库(1994～2004),获取关于网络成瘾研究方面的所有相关文献。资料选择:对获取的资料筛选出综述主题文献,并对重点文献的主要参考文献进行检索追踪相关全文。资料提炼:共收集到29篇符合标准的相关文献,对其进行分类整理,其中11篇选用为参考文献。资料综合:近年来对网络成瘾现象的研究主要集中在对该现象的定性描述和理论探讨上,缺少对网络成瘾行为背后的心理机制和行为动机的理论研究和实验论证。结论:网络成瘾是一种心理上的对网络的依赖,需要从心理层面进行探讨。研究者应当从网络本身的特点及被试者自身的心理特征以及社会因素出发,对网络成瘾这一概念进行科学、准确的界定。基于这一界定,根据心理测量学的原则,编制出严格科学的测量工具。研究者还应当综合运用调查研究、个案研究和实验研究等多种研究方法,对网络成瘾的特点及形成机制进行深入全面的探讨。     

Sustained deep-tissue pain alters functional brain connectivity

Recent functional brain connectivity studies have contributed to our understanding of the neurocircuitry supporting pain perception. However, evoked-pain connectivity studies have employed cutaneous and/or brief stimuli, which induce sensations that differ appreciably from the clinical pain experience. Sustained myofascial pain evoked by pressure cuff affords an excellent opportunity to evaluate functional connectivity change to more clinically relevant sustained deep-tissue pain. Connectivity in specific networks known to be modulated by evoked pain (sensorimotor, salience, dorsal attention, frontoparietal control, and default mode networks: SMN, SLN, DAN, FCN, and DMN) was evaluated with functional-connectivity magnetic resonance imaging, both at rest and during a sustained (6-minute) pain state in healthy adults. We found that pain was stable, with no significant changes of subjects’ pain ratings over the stimulation period. Sustained pain reduced connectivity between the SMN and the contralateral leg primary sensorimotor (S1/M1) representation. Such SMN–S1/M1 connectivity decreases were also accompanied by and correlated with increased SLN–S1/M1 connectivity, suggesting recruitment of activated S1/M1 from SMN to SLN. Sustained pain also increased DAN connectivity to pain processing regions such as mid-cingulate cortex, posterior insula, and putamen. Moreover, greater connectivity during pain between contralateral S1/M1 and posterior insula, thalamus, putamen, and amygdala was associated with lower cuff pressures needed to reach the targeted pain sensation. These results demonstrate that sustained pain disrupts resting S1/M1 connectivity by shifting it to a network known to process stimulus salience. Furthermore, increased connectivity between S1/M1 and both sensory and affective processing areas may be an important contribution to interindividual differences in pain sensitivity.     

Large-scale directional connections among multi resting-state neural networks in human brain: a functional MRI and Bayesian network modeling study

This study examined the large-scale connectivity among multiple resting-state networks (RSNs) in the human brain. Independent component analysis was first applied to the resting-state functional MRI (fMRI) data acquired from 12 healthy young subjects for the separation of RSNs. Four sensory (lateral and medial visual, auditory, and sensory-motor) RSNs and four cognitive (default-mode, self-referential, dorsal and ventral attention) RSNs were identified. Gaussian Bayesian network (BN) learning approach was then used for the examination of the conditional dependencies among these RSNs and the construction of the network-to-network directional connectivity patterns. The BN based results demonstrated that sensory networks and cognitive networks were hierarchically organized. Specially, we found the sensory networks were highly intra-dependent and the cognitive networks were strongly intra-influenced. In addition, the results depicted dominant bottom-up connectivity from sensory networks to cognitive networks in which the self-referential and the default-mode networks might play respectively important roles in the process of resting-state information transfer and integration. The present study characterized the global connectivity relations among RSNs and delineated more characteristics of spontaneous activity dynamics.     

Non-parametric model selection for subject-specific topological organization of resting-state functional connectivity

In recent years, graph theory has been successfully applied to study functional and anatomical connectivity networks in the human brain. Most of these networks have shown small-world topological characteristics: high efficiency in long distance communication between nodes, combined with highly interconnected local clusters of nodes. Moreover, functional studies performed at high resolutions have presented convincing evidence that resting-state functional connectivity networks exhibits (exponentially truncated) scale-free behavior. Such evidence, however, was mostly presented qualitatively, in terms of linear regressions of the degree distributions on log-log plots. Even when quantitative measures were given, these were usually limited to the r(2) correlation coefficient. However, the r(2) statistic is not an optimal estimator of explained variance, when dealing with (truncated) power-law models. Recent developments in statistics have introduced new non-parametric approaches, based on the Kolmogorov-Smirnov test, for the problem of model selection. In this work, we have built on this idea to statistically tackle the issue of model selection for the degree distribution of functional connectivity at rest. The analysis, performed at voxel level and in a subject-specific fashion, confirmed the superiority of a truncated power-law model, showing high consistency across subjects. Moreover, the most highly connected voxels were found to be consistently part of the default mode network. Our results provide statistically sound support to the evidence previously presented in literature for a truncated power-law model of resting-state functional connectivity.     

Visual stimulation and frequency of focal neurological symptoms engage distinctive neurocognitive resources in migraine with aura patients: a study of resting-state functional networks

IntroductionSeveral functional neuroimaging studies on healthy controls and patients with migraine with aura have shown that the activation of functional networks during visual stimulation is not restricted to the striate system, but also includes several extrastriate networks.MethodsBefore and after 4 min of visual stimulation with a checkerboard pattern, we collected functional MRI in 21 migraine with aura (MwA) patients and 18 healthy subjects (HS). For each recording session, we identified independent resting-state networks in each group and correlated network connection strength changes with clinical disease features.ResultsBefore visual stimulation, we found reduced connectivity between the default mode network and the left dorsal attention system (DAS) in MwA patients compared to HS. In HS, visual stimulation increases functional connectivity between the independent components of the bilateral DAS and the executive control network (ECN). In MwA, visual stimulation significantly improved functional connectivity between the independent component pairs salience network and DAS, and between DAS and ECN. The ECN Z-scores after visual stimulation were negatively related to the monthly frequency of aura.ConclusionsIn individuals with MwA, 4 min of visual stimulation had stronger cognitive impact than in healthy people. A higher frequency of aura may lead to a diminished ability to obtain cognitive resources to cope with transitory but important events like aura-related focal neurological symptoms.     

Early Diagnosis of Complex Diseases by Molecular Biomarkers,Network Biomarkers,and Dynamical Network Biomarkers

Many studies have been carried out for early diagnosis of complex diseases by finding accurate and robust biomarkers specific to respective diseases. In particular, recent rapid advance of high‐throughput technologies provides unprecedented rich information to characterize various disease genotypes and phenotypes in a global and also dynamical manner, which significantly accelerates the study of biomarkers from both theoretical and clinical perspectives. Traditionally, molecular biomarkers that distinguish disease samples from normal samples are widely adopted in clinical practices due to their ease of data measurement. However, many of them suffer from low coverage and high false‐positive rates or high false‐negative rates, which seriously limit their further clinical applications. To overcome those difficulties, network biomarkers (or module biomarkers) attract much attention and also achieve better performance because a network (or subnetwork) is considered to be a more robust form to characterize diseases than individual molecules. But, both molecular biomarkers and network biomarkers mainly distinguish disease samples from normal samples, and they generally cannot ensure to identify predisease samples due to their static nature, thereby lacking ability to early diagnosis. Based on nonlinear dynamical theory and complex network theory, a new concept of dynamical network biomarkers (DNBs, or a dynamical network of biomarkers) has been developed, which is different from traditional static approaches, and the DNB is able to distinguish a predisease state from normal and disease states by even a small number of samples, and therefore has great potential to achieve “real” early diagnosis of complex diseases. In this paper, we comprehensively review the recent advances and developments on molecular biomarkers, network biomarkers, and DNBs in particular, focusing on the biomarkers for early diagnosis of complex diseases considering a small number of samples and high‐throughput data (or big data). Detailed comparisons of various types of biomarkers as well as their applications are also discussed.     

Functional Connectivity of the Default Mode Network and Its Association With Pain Networks in Irritable Bowel Patients Assessed via Lidocaine Treatment

The default mode network (DMN), a group of brain regions implicated in passive thought processes, has been proposed as a potentially informative neural marker to aid in novel treatment development. However, the DMN's internal connectivity and its temporal relationship (ie, functional network connectivity) with pain-related neural networks in chronic pain conditions is poorly understood, as is the DMN's sensitivity to analgesic effects. The current study assessed how DMN functional connectivity and its temporal association with 3 pain-related networks changed after rectal lidocaine treatment in irritable bowel syndrome patients. Eleven females with irritable bowel syndrome underwent a rectal balloon distension paradigm during functional magnetic resonance imaging in 2 conditions: natural history (ie, baseline) and lidocaine. Results showed increased DMN connectivity with pain-related regions during natural history and increased within-network connectivity of DMN structures under lidocaine. Further, there was a significantly greater lag time between 2 of the pain networks, those involved in cognitive and in affective pain processes, comparing lidocaine to natural history. These findings suggest that 1) DMN plasticity is sensitive to analgesic effects, and 2) reduced pain ratings via analgesia reflect DMN connectivity more similar to pain-free individuals. Findings show potential implications of this network as an approach for understanding clinical pain management techniques.     

Adaptive cardiac resynchronization therapy device: a simulation report

We report the results of a simulation of an adaptive cardiac resynchronization therapy (CRT) device performing biventricular pacing in which the atrioventricular (AV) delay and interventricular (VV) interval parameters are changed dynamically in response to data provided by the simulated IEGMs and simulated hemodynamic sensors. A learning module, an artificial neural network, performs the adaptive part of the algorithm supervised by an algorithmic deterministic module, internally or externally from the implanted CRT or CRT-D. The simulated cardiac output obtained with the adaptive CRT device is considerably higher (30%) especially with higher heart rates than in the nonadaptive CRT mode and is likely to be translated into improvement in quality of life of patients with congestive heart failure.     

Sleep deprivation reduces default mode network connectivity and anti-correlation during rest and task performance

Sleep deprivation (SD) can alter extrinsic, task-related fMRI signal involved in attention, memory and executive function. However, its effects on intrinsic low-frequency connectivity within the Default Mode Network (DMN) and its related anti-correlated network (ACN) have not been well characterized. We investigated the effect of SD on functional connectivity within the DMN, and on DMN-ACN anti-correlation, both during the resting state and during performance of a visual attention task (VAT). 26 healthy participants underwent fMRI twice: once after a normal night of sleep in rested wakefulness (RW) and once following approximately 24 h of total SD. A seed-based approach was used to examine pairwise correlations of low-frequency fMRI signal across different nodes in each state. SD was associated with significant selective reductions in DMN functional connectivity and DMN-ACN anti-correlation. This was congruent across resting state and VAT analyses, suggesting that SD induces a robust alteration in the intrinsic connectivity within and between these networks.     

Signed graph representation learning for functional-to-structural brain network mapping

MRI-derived brain networks have been widely used to understand functional and structural interactions among brain regions, and factors that affect them, such as brain development and diseases. Graph mining on brain networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Since brain functional and structural networks describe the brain topology from different perspectives, exploring a representation that combines these cross-modality brain networks has significant clinical implications. Most current studies aim to extract a fused representation by projecting the structural network to the functional counterpart. Since the functional network is dynamic and the structural network is static, mapping a static object to a dynamic object may not be optimal. However, mapping in the opposite direction (i.e., from functional to structural networks) are suffered from the challenges introduced by negative links within signed graphs. Here, we propose a novel graph learning framework, named as Deep Signed Brain Graph Mining or DSBGM, with a signed graph encoder that, from an opposite perspective, learns the cross-modality representations by projecting the functional network to the structural counterpart. We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets (HCP and OASIS). Our experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods.