Altered topological organization of brain structural covariance networks has been observed in attention deficit hyperactivity disorder (ADHD). However, results have been inconsistent, potentially related to confounding medication effects. In addition, since structural networks are traditionally constructed at the group level, variabilities in individual structural features remain to be well characterized. Structural brain imaging with MRI was performed on 84 drug-naive children with ADHD and 83 age-matched healthy controls. Single-subject gray matter (GM) networks were obtained based on areal similarities of GM, and network topological properties were analyzed using graph theory. Group differences in each topological metric were compared using nonparametric permutation testing. Compared with healthy subjects, GM networks in ADHD patients demonstrated significantly altered topological characteristics, including higher global and local efficiency and clustering coefficient, and shorter path length. In addition, ADHD patients exhibited abnormal centrality in corticostriatal circuitry including the superior frontal gyrus, orbitofrontal gyrus, medial superior frontal gyrus, precentral gyrus, middle temporal gyrus, and pallidum (all p < .05, false discovery rate [FDR] corrected). Altered global and nodal topological efficiencies were associated with the severity of hyperactivity symptoms and the performance on the Stroop and Wisconsin Card Sorting Test tests (all p < .05, FDR corrected). ADHD combined and inattention subtypes were differentiated by nodal attributes of amygdala (p < .05, FDR corrected). Alterations in GM network topologies were observed in drug-naive ADHD patients, in particular in frontostriatal loops and amygdala. These alterations may contribute to impaired cognitive functioning and impulsive behavior in ADHD.

Recently, graph theoretical approaches applied to neuroimaging data have advanced understanding of the human brain connectome and its abnormalities in psychiatric disorders. However, little is known about the topological organization of brain white matter networks in posttraumatic stress disorder (PTSD). Seventy-six patients with PTSD and 76 age, gender, and years of education-matched trauma-exposed controls were studied after the 2008 Sichuan earthquake using diffusion tensor imaging and graph theoretical approaches. Topological properties of brain networks including global and nodal measurements and modularity were analyzed. At the global level, patients showed lower clustering coefficient (p = .016) and normalized characteristic path length (p = .035) compared with controls. At the nodal level, increased nodal centralities in left middle frontal gyrus, superior and inferior temporal gyrus and right inferior occipital gyrus were observed (p < .05, corrected for false-discovery rate). Modularity analysis revealed that PTSD patients had significantly increased inter-modular connections in the fronto-parietal module, fronto-striato-temporal module, and visual and default mode modules. These findings indicate a PTSD-related shift of white matter network topology toward randomization. This pattern was characterized by an increased global network integration, reflected by increased inter-modular connections with increased nodal centralities involving fronto-temporo-occipital regions. This study suggests that extremely stressful life experiences, when they lead to PTSD, are associated with large-scale brain white matter network topological reconfiguration at global, nodal, and modular levels.

Background Previous diabetes mellitus studies of cognitive impairments in the early stages have focused on changes in brain structure and function, and more recently the focus has shifted to the relationships between encephalic regions and diversification of network topology. However, studies examining network topology in diabetic brain function are still limited. Methods The study included 102 subjects; 55 type 2 diabetes mellitus (T2DM) patients plus 47 healthy controls. All subjects were examined by resting-state functional magnetic resonance imaging (rs-fMRI) scan. According to Automated Anatomical Labeling, the brain was divided into 90 anatomical regions, and every region corresponds to a brain network analysis node. The whole brain functional network was constructed by thresholding the correlation matrices of the 90 brain regions, and the topological properties of the network were computed based on graph theory. Then, the topological properties of the network were compared between different groups by using a non-parametric test. Finally, the associations between differences in topological properties and the clinical indicators were analyzed. Results The brain functional networks of both T2DM patients and healthy controls were found to possess small-world characteristics, i.e., normalized clustering coefficient (γ) > 1, and normalized characteristic path length (λ) close to 1. No significant differences were found in the small-world characteristics (σ). Second, the T2DM patient group displayed significant differences in node properties in certain brain regions. Correlative analytic results showed that the node degree of the right inferior temporal gyrus (ITG) and the node efficiencies of the right ITG and superior temporal gyrus of T2DM patients were positively correlated with body mass index. Conclusion The brain network of T2DM patients has the same small-world characteristics as normal people, but the normalized clustering coefficient is higher and the normalized characteristic path length is lower than that of the normal control group, indicating that the brain function network of the T2DM patients has changed. The changes of node properties were mostly concentrated in frontal lobe, temporal lobe and posterior cingulate gyrus. The abnormal changes in these indices in T2DM patients might be explained as a compensatory behavior to reduce cognitive impairments, which is achieved by mobilizing additional neural resources, such as the excessive activation of the network and the efficient networking of multiple brain regions.

Introduction: Disrupted topological organization of brain functional networks has been widely observed in posttraumatic stress disorder (PTSD). However, the topological organization of the brain grey matter (GM) network has not yet been investigated in pediatric PTSD who was more vulnerable to develop PTSD when exposed to stress. Materials and methods: Twenty two pediatric PTSD patients and 22 matched trauma-exposed controls who survived a massive earthquake (8.0 magnitude on Richter scale) in Sichuan Province of western China in 2008 underwent structural brain imaging with MRI 8-15 months after the earthquake. Brain networks were constructed based on the morphological similarity of GM across regions, and analyzed using graph theory approaches. Nonparametric permutation testing was performed to assess group differences in each topological metric. Results: Compared with controls, brain networks of PTSD patients were characterized by decreased characteristic path length (P = 0.0060) and increased clustering coefficient (P = 0.0227), global efficiency (P = 0.0085) and local efficiency (P = 0.0024). Locally, patients with PTSD exhibited increased centrality in nodes of the default-mode (DMN), central executive (CEN) and salience networks (SN), involving medial prefrontal (mPFC), parietal, anterior cingulate (ACC), occipital and olfactory cortex and hippocampus. Conclusions: Our analyses of topological brain networks in children with PTSD indicate a significantly more segregated and integrated organization. The associations and disassociations between these grey matter findings and white matter (WM) and functional changes previously reported in this sample may be important for diagnostic purposes and understanding the brain maturational effects of pediatric PTSD.

To psychoradiologically investigate the topological organization of single‐subject gray matter networks in patients with PTSD. Eighty‐nine adult PTSD patients and 88 trauma‐exposed controls (TEC) underwent a structural T1 magnetic resonance imaging scan. The single‐subject brain structural networks were constructed based on gray matter similarity of 90 brain regions. The area under the curve (AUC) of each network metric was calculated and both global and nodal network properties were measured in graph theory analysis. We used nonparametric permutation tests to identify group differences in topological metrics. Relationships between brain network measures and clinical symptom severity were analyzed in the PTSD group. Compared with TEC, brain networks of PTSD patients were characterized by decreased clustering coefficient (Cp) (p = .04) and local efficiency (Eloc) (p = .04). Locally, patients with PTSD exhibited altered nodal centrality involving medial superior frontal (mSFG), inferior orbital frontal (iOFG), superior parietal (SPG), middle frontal (MFG), angular, and para‐hippocampal gyri (p < .05, corrected). A negative correlation between the segregation (Cp) of gray matter and functional networks was found in PTSD patients but not the TEC group. Analyses of topological brain gray matter networks indicate a more randomly organized brain network in PTSD. The reduced segregation in gray matter networks and its negative relation with increased segregation in the functional network indicate an inverse relation between gray matter and functional changes. The present psychoradiological findings may reflect a compensatory increase in functional network segregation following a loss of segregation in gray matter networks.

This study investigated the mental health of military transport personnel in the Western Sichuan Plateau of China, and factors that correlate with their mental health.The Symptom Checklist 90 (SCL-90) was used to investigate the mental health status of the subjects. Their scores were compared with the national and military norm in China. Demographic factors were analyzed for associations with SCL-90 scores.Psychological problems were detected in 28.90% of total 1076 male officers and soldiers surveyed. The SCL-90 scale somatization score of these servicemen was higher than the national and military norms in China, while other scores were comparable. The reported physical health symptoms and being an only child were strongly associated with the SCL-90 scores.The mental health of military transport personnel in the China Western Sichuan Plateau should receive more attention.

Neuroimaging studies in children and adolescents with post-traumatic stress disorder (PTSD) have focused on abnormal structures and the functionality of a few individual brain regions. However, little is known about alterations to the topological organization of whole-brain functional networks in children and adolescents with PTSD. To this end, we investigated the topological properties of brain functional networks derived from resting-state functional magnetic resonance imaging (r-fMRI) in patients suffering from PTSD. The r-fMRI data were obtained from 10 PTSD patients and 16 trauma-exposed non-PTSD subjects. Graph theory analysis was used to investigate the topological properties of the two groups, and group comparisons of topological metrics were performed using nonparametric permutation tests. Both the PTSD and non-PTSD groups showed the functional brain network to have a small-world architecture. However, the PTSD group exhibited alterations in global properties characterized by higher global efficiency, lower clustering coefficient, and characteristic path length, implying a shift toward randomization of the networks. The PTSD group also showed increased nodal centralities, predominately in the left middle frontal gyrus, caudate nucleus, and hippocampus, and decreased nodal centralities in the left anterior cingulate cortex, left paracentral lobule, and bilateral thalami. In addition, the clustering coefficient and nodal betweenness of the left paracentral lobule were found to be negatively and positively correlated with the re-experiencing and hyper-arousal symptoms of PTSD respectively. The findings of disrupted topological properties of functional brain networks may help to better understand the pathophysiological mechanism of PTSD in children and adolescents.

Primary, objective: To investigate the epidemiological characteristics of paediatric inpatients, with traumatic brain injury (TBI) in China. Research design: The Chinese Trauma Database (CTD), a nationwide register system based on hospital admission data; contains diagnosis and treatment information for trauma inpatients in over 200 military-managed public-service hospitals in China. Using the ICD-9 coding, the data for Children with TBI aged 0-17 years between 2001 and 2007 were retrieved'. Methods and procedures: The demographic characteristics, admission time, injury cause, severity and treatment outcomes of paediatric inpatients with TBI were analysed. Main outcomes and results: A total of 26,028 paediatric inpatients with TBI (69.52% male, 30.48% female) were included in the CTD. Motor vehicle traffic (MVT) accidents, falls and assaults were the primary causes of injury. Falls were, the leading cause of TBI in children aged 0-4 years, and MVT was the leading cause of TBI in children aged 5-17 years. According to the abbreviated injury scale, 37.20% of the TBI cases were mild, 25.15% were moderate, 24.81% were severe and 12.84% were critically severe. Conclusion: Chinese authorities, should develop targeted measures to reduce children injuries based on the leading causes of TBI in the different age groups, particularly MVT, falls and assaults.

Direction of arrival(DOA) estimate has become the key issues in mobile communication.When the user's signal direction is unknown,it is feasible to estimate signal DOA by the multiple signal classification (MUSIC) algorithm and estimating signal parameters viarotational invariance techniques (ESPRIT) algorithm.It takes different methods to analyse different signals.For narrow-band signal, the various factors affecting the MUSIC algorithm, such as the signal to noise ratio,array element number, and the number of snapshots,are analyzed by experiments.So does ESPRIT algorithm.It also makes comparison and analysis betwen MUSIC and TL-ESPRIT algorithms.For wide-band signal, this paper studies the DOA estimation with emphasis on incoherent signal-subspace processing method which the narrow sub-band signals with low signal-to-noise ratios are slightly weighted or discarded.Through simulation experiments, the advantages of the improved algorithms are demonstrated.Furthermore the applications for the two improved algorithms are analyzed.

在利用电话进行通信时,有时候语言的描述可能并不能很好地表达所传递的信息,如果此时双方能够及时地开启一个画板实时传输数据,在画板上手绘图表等信息来帮助表达意图,可提高通信的质量和效率.基于Android平台实现了画板间的实时通信功能,可在不同网络情况下实时传输画板数据.该画板是将移动设备作为手绘输入板,用户可以通过手指触摸屏来实现绘图等功能.为了确保无论用户在什么样的网络环境下都能够有效通信,文中提供了三个不同的解决方案.重点描述了当通信双方在不同局域网时,利用UDP打洞传输数据的通信方式.

To explore the differences of effective connectivity while verbal generation task is respectively performed by people in Chi -nese (NH group) as well as in Uighur (NW group).Functional MRI data was acquired when verbal generation task was conducted on n-ative Chinese and native Uighur volunteers .After detecting the activated brain region , we selected the left Caudate (CAU.L) as ROI(re-gion of interesting ) , then chose the Granger Causality Analysis to perform the effective connectivity analysis .Compared with NW group , NH group showed increased inflows from the right frontal and parietal lobe , as well as the left frontal , limbic and occipital cortex to CAU.L.Besides, NH group also showed increased outflows from CAU .L to the right cingulate cortex and middle temporal cortex .While performing the verbal generation task , NH group shows increased effective connectivity between CAU .L and the regions related to visual sense, memory and semantics .NH group invokes more brain regions and shows more complicated information flows while carrying out the verbal generation task , it is presumed that NH group exists a more complex neural mechanism of language than NW group .

Radiology: Volume 000: Number 0—   2017 n radiology.rsna.org 1 1 From the Huaxi MR Research Center (HMRRC), Department of Radiology (X.S., D.L., Q.G.), and Department of Neurology (N.L., L.C., R.P.), West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, Sichuan 610041, China; Department of Medical Information Engineering, School of Electrical Engineering and Information, Sichuan University, Chengdu, Sichuan, China (F.C.); Department of Radiology, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China (M.W.); and Department of Musculoskeletal Biology and MRC-Arthritis Research UK Centre for Integrated Research into Musculoskeletal Ageing (CIMA), Faculty of Health and Life Sciences, University of Liverpool, Liverpool, England (G.J.K.). Received December 12, 2016; revision requested March 16, 2017; revision received May 20; accepted June 17; final version accepted June 27. Address correspondence to Q.G. (e-mail: qiyonggong@ hmrrc.org.cn).

Purpose To use resting-state functional magnetic resonance (MR) imaging and graph theory approaches to investigate the brain functional connectome and its potential relation to disease severity in Parkinson disease (PD). Materials and Methods This case-control study was approved by the local research ethics committee, and all participants provided informed consent. There were 153 right-handed patients with PD and 81 healthy control participants recruited who were matched for age, sex, and handedness to undergo a 3-T resting-state functional MR examination. The whole-brain functional connectome was constructed by thresholding the Pearson correlation matrices of 90 brain regions, and the topologic properties were analyzed by using graph theory approaches. Nonparametric permutation tests were used to compare topologic properties, and their relationship to disease severity was assessed. Results The functional connectome in PD showed abnormalities at the global level (ie, decrease in clustering coefficient, global efficiency, and local efficiency, and increase in characteristic path length) and at the nodal level (decreased nodal centralities in the sensorimotor cortex, default mode, and temporal-occipital regions; P < .001, false discovery rate corrected). Further, the nodal centralities in left postcentral gyrus and left superior temporal gyrus correlated negatively with Unified Parkinson's Disease Rating Scale III score (P = .038, false discovery rate corrected, r = -0.198; and P = .009, false discovery rate corrected, r = -0.270, respectively) and decreased with increasing Hoehn and Yahr stage in patients with PD. Conclusion The configurations of brain functional connectome in patients with PD were perturbed and correlated with disease severity, notably with those responsible for motor functions. These results provide topologic insights into understanding the neural functional changes in relation to disease severity of PD. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on September 11, 2017.

To recognize emotional states accurately and effectively, preprocessing and feature extraction for multi-physiological signals (ECG,EMG,RSP,SC) in terms of four emotions(Joy, Anger, Sadness, Pleasure) were undertaken, ReliefF algorithm was used for feature selection, and J48 decision tree classifier was used to achieve recognition of the four emotional states.Average recognition rate based on J48 decision tree classifier of the four emotional states is 96.74%, and analyzed results show that RSP signal is very important for emotional states recognition.Combinations with different physiological signals for emotional states recognition have different effects.The error recognition rate between Sadness and Pleasure is relatively high.The number of features J48 decision tree used for classification is positively correlated with the number of samples.

The incidence of traumatic brain injury (TBI) among older people has been significantly increased. Understanding the epidemiological characteristics, the causes of injury and the outcomes of TBI among older people provides value evidence for designing preventative measures and therapeutic interventions for TBI. In this study, using ICD-9-CM codes in the Chinese Trauma Database, we identified inpatients aged 65years and older diagnosed with TBI between 2001 and 2007 in Chinese military hospitals. Demographic characteristics, admission time, injury cause, injury severity, length of stay, hospitalization costs, and outcomes were systematically described. In total, 13,802 inpatients with TBI (63.13% males and 36.87% females) were identified from over 200 Chinese military hospitals. TBI diagnoses increased by a mean yearly rate of 7.78%; the annual admission peaked during the third quarter of the year, October in particular. The leading causes of TBI were motor vehicle traffic (48.37%) and fall-related incidents (38.89%). The severity of TBI in older inpatients was classified by the Abbreviated Injury Scale as minor (24.31%), serious (19.37%), severe (37.95%), and critical (18.37%). The mean length of stay was 17.87±23.31days, the median hospitalization cost was US$795, and the case fatality rate was 9.38%. These data substantiate that the design and implementation of proven and cost-effective preventive measures focusing on the leading causes of TBI in this population is essential.

Traumatic brain injury (TBI) poses a serious global public health concern. Recent studies on TBI epidemiology have been conducted in China and East China. Southwest China is economically underdeveloped, and data on TBI in this region are scarce. The present study aimed to evaluate the characteristics of TBI inpatients from military hospitals in Southwest China. We used data from the Chinese Trauma Database (CTD), which contained information on diagnosis and treatment of trauma patients hospitalized in more than 200 military hospitals in China. Based on the ICD-10 code for TBI, we retrieved information on civilian inpatients with TBI at 16 military hospitals in Southwest China between January 2008 and December 2012 and performed a comprehensive analysis of demographics, cause of injury, severity of injury, length of stay (days), inpatient costs, and mortality rate in patients with TBI. In total, data of 36,413 TBI inpatients were included. Hospital admissions grew at an average annual rate of 5.32%. The mean patient age was 35.29 +/- 17.39 years (76.23% men, 23.77% women). Motor vehicle traffic (MVT) accidents (45.92%), falls (25.41%), and assaults (18.09%) were the 3 leading causes of TBI. According to the Abbreviated Injury Scale (AIS) score, 60.40% of TBI cases were of mild, 24.02% were of moderate, and 15.58% were of severe intensity. The mean length of stay was 13.83 +/- 19.08 days, median inpatient cost was USD 735, and mortality rate was 2.54%. In conclusion, TBI is an important public health problem in Southwest China. A governmental initiative is expediently needed to establish a TBI monitoring system that covers the southwest region to develop more effective and targeted measures for TBI prevention, treatment, and rehabilitation.

大脑是一个高度复杂的神经网络系统,随着磁共振成像技术的快速发展,目前研究逐步深入到人类大脑并行神经网络和不同脑区间的信息流,以更全面深入地探索脑功能机制.有效连接分析是近年来在脑功能网络方面的一个研究热点.对人脑功能网络进行有效连接分析,可能对神经精神疾病的病理机制和脑功能异常状态下的功能逻辑有着更好的理解.本文着重对有效连接的几种经典方法(包括结构方程模型、多变量自回归模型、格兰杰因果分析和动态因果模型)的算法原理、存在的问题、算法比较、最新发展及在大脑功能磁共振成像数据中的应用做简要介绍.

Purpose: To use DTI and graph theory approaches to explore the brain structural connectome in pediatric posttraumatic stress disorder (PTSD). Materials and Methods: This study was approved by the relevant research ethics committee, and all subjects’ parents/guardians provided informed consent. Twenty-four pediatric PTSD patients and 23 trauma-exposed non-PTSD controls were recruited after the 2008 Sichuan earthquake. The structural connectome was constructed using diffusion tensor imaging tractography, by thresholding the mean fractional anisotropy of 90 brain regions to yield 90×90 partial correlation matrixes. Graph theory analysis was used to examine the group-specific topological properties, and nonparametric permutation tests were used for group comparisons of topological metrics. Results: Both groups exhibited small-world topology. However, PTSD showed increase in the characteristic path length (Lp) (P=0.0248), and decrease in local efficiency (Eloc) (P=0.0498) and global efficiency (Eglob) (P=0.0274). Furthermore, PTSD showed reduced nodal centralities mainly in the default mode, salience, central executive and visual regions (P < 0.05, false discovery rate corrected). The Clinician-Administered PTSD Scale score was negatively correlated with nodal efficiency of left superior parietal gyrus (P=0.043). Conclusion: The structural connectome showed a shift toward ‘regularization’, providing a structural basis for functional alterations of pediatric PTSD. These abnormalities suggest that PTSD can be understood by examining the dysfunction of large-scale spatially distributed neural networks. Introduction Brain structure can be interpreted as an integrated network using concepts of graph theory which quantify the whole brain as a single graph, comprising nodes linked by edges (1). Specifically, the small-world network pattern, which seems to have evolved to mediate high-efficiency parallel information transfer (2), has been used to define pathology in psychiatric disorders, including posttraumatic stress disorder (PTSD) (3-5). PTSD is a traumaand stressor-related disorder characterized by four symptom clusters: re-experience, avoidance, negative cognitions and mood, and arousal (6). Pediatric PTSD is not uncommon, the prevalence among children (12–17 years) being 3.7% for boys and 6.3% for girls (7). Childhood trauma is a severe stressor with multiple neurochemical and hormonal effects which can lead to lasting changes in brain structure and function (8). Children are particularly susceptible to PTSD (9), which may adversely influence brain development. Early interventions may help prevent brain changes. Most early structural neuroimaging studies investigating the impact of childhood trauma on white matter (WM) integrity in children (10, 11) used manual tracing or volumetric morphometry. Diffusion tensor imaging (DTI) has since emerged as a powerful technique to assess WM tracts by exploiting the diffusion of tissue water. Using DTI we identified whole-brain WM microstructural abnormalities in pediatric PTSD (12), but we did not explore the neurocircuitry directly using connectivity analyses. We also applied graph theory to functional magnetic resonance imaging (MRI) data, finding that the functional connectome in pediatric PTSD is shifted toward ‘regularization’ (from a small-world to a more regular network) (4). As functional interaction is constrained by brain cortical anatomy (13), to fully understand function one must study its structural substrate directly. Based on our findings in the functional connectome (4), we hypothesized that in pediatric PTSD the structural connectome would show a similarly disrupted topological organization. Our purpose was to use DTI and graph theory approaches to explore the brain structural connectome in pediatric posttraumatic stress disorder (PTSD). Materials and Methods Participants This study was approved by the local research ethics committee. Each child’s parent/guardian was given a detailed information sheet, and then gave written consent. A total of 4,200 earthquake survivors were screened by M.W. and X.W. 8-15 months after the 8.0 magnitude earthquake in Sichuan in May 2008. Each participant was interviewed and screened using the PTSD checklist (PCL) (14); those scoring >35 on PCL were given the Clinician-Administered PTSD Scale (CAPS) (15) by a psychiatrist (L.L., 31 years’ experience), of which those scoring >50 on CAPS were diagnosed with PTSD; those scoring <30 on PCL were considered non-PTSD trauma-exposed controls and were not assessed using CAPS (16). Inclusion criteria for all participants were: personal experience of the earthquake; personal witness of death, serious injury, or building collapse; age <18 years; IQ >80. This identified 161 PTSD patients and 99 trauma-exposed non-PTSD controls with similar demographic characteristics, lifestyle and earthquake experiences. Exclusion criteria were: psychiatric co-morbidities assessed using the Structured Clinical Interview for DSM-IV (17); history of psychiatric or neurological disorders (n=42); MRI contraindication (n=30); recent medication that might affect brain function (n=24); unavailability of key data (n=12); left-handedness (n=10); CAPS score >35 but <50 (n=8); history of or current brain injury (n=7). Twenty-eight drug-naïve first-episode PTSD patients and 26 trauma-exposed non-PTSD controls underwent MRI scanning. Head-motion artifacts excluded data from 4 PTSD and 3 controls. MRI data from 24 PTSD and 23 controls went forward for analysis. We have reported elsewhere some other MR data from some of these subjects. In (4) we reported resting state functional MRI data of 24 PTSD and 24 controls: that work investigated the brain functional connectome, while our current work explores the brain structural connectome. In (12) we reported DTI data of 27 PTSD and 24 controls: that work investigated the microstructural networks using voxel-based analysis, while our current work explores the structural network directly using connectivity analyses. Data Acquisition MRI data were acquired on a 3T MRI system (EXCITE; General Electric) using a single-shot spin-echo echo planar image (SE-EPI) sequence, and included one high-resolution T1 scan and one DTI data scan. Foam padding was used to minimize head motion. A whole-brain high-resolution T1-weighted image was acquired using a sagittal three-dimensional spoiled gradient recall (SPGR) sequence with repetition time (TR) = 8.5 ms, echo time (TE) = 3.4 ms, inversion time (TI) = 400 ms, slice thickness = 1 mm, no inter-slice gap, 156 axial slices, matrix size = 256×256, field of view (FOV) = 24×24 cm and flip angle = 12°. The diffusion sensitizing gradients were applied along 15 non-collinear directions (b-value = 1000 s/mm) together with an acquisition without diffusion weighting (b = 0). Imaging parameters were TR = 12000 ms, TE = 71.6 ms, number of excitations (NEX) = 2, slice thickness = 3 mm, 50 slices, 128×128 matrix and 24×24 cm FOV. The protocol included susceptibility-weighted imaging (SWI) which will be analyzed in a future study and fluid attenuated inversion recovery (FLAIR) sequences which were evaluated for clinical abnormalities by a neuroradiologist. A radiologist (L.S., with 3 years experience) evaluated and verified image quality. Data Pre-Processing and DTI-Based Structural Network Construction All the image preprocessing and analyses were implemented using a pipeline tool for diffusion MRI (PANDA) (18). We (X.S. and D.L.) extracted the fractional anisotropy (FA) map of each subject in 3 steps: BET (skull removal), eddy correct and DTIFIT (building diffusion tensor models). We then registered the FA maps with the FMRIB FA template in standard MNI space using nonlinear registration. The automated anatomic labeling atlas (90 regions) was used to define the nodes of the WM network. PANDA uses the procedure proposed by Gong et al (19). Briefly, each of the individual FA images in native space was co-registered to its corresponding T1-weighted image using an affine transformation. Then the transformed T1-weighted images were non-linearly registered to the MNI space. The inverse transformations were obtained to the above two steps to transform the automated anatomic labeling atlas from MNI space to DTI native space. Thus, the individual cerebrum in native space was divided into 90 nodes corresponding to the automated anatomic labeling atlas. Each node represents a region of the DTI-based structural brain network. Deterministic tractography was performed to reconstruct whole brain WM tracts using the Fiber Assignment by Continuous Tracking algorithm (20). A tract was terminated if the turn angle was >45° or the fiber entered a voxel with FA <0.2 (21). We defined the averaged FA of the linking fibers for each connection. For each individual, we generated a symmetric 90×90 network matrix in which each row/column represents a brain node/region and each element represents the averaged FA of the linking fibers between nodes. Using the GRETNA toolbox we investigated the topological properties of brain networks at both the global and nodal level. The global level properties were of two kinds: small-world parameters [for definitions see (2)], including the clustering coefficient Cp, characteristic path length Lp [calculated as the harmonic mean distance between all possible pairs of regions to address the disconnected graphs dilemma(22)], normalized clustering coefficient γ, normalized characteristic path length λ, and small-worldness σ; and network efficiency parameters [for definitions see (23)], including the local efficiency Eloc and global efficiency Eglob. The nodal level properties were the nodal degree, nodal efficiency, and nodal betweenness. For each network metric we calculated the area under the curve (AUC) over the sparsity range from S1 to Sn with an interval of ΔS, where S1 = 0.10, Sn = 0.34 and ΔS = 0.01. The AUC provides a summarized scalar for t

Since existing operations of MRI image preprocessing are too complicated or excessively rely upon the compiler environment,it seriously affects the processing efficiency and time-consuming.In view of this,the paper introduces two MRI neuroimaging picture preprocess-ing algorithms,both are based on mixed C ++and MATLAB platform calling,and their applications.By calling bet.exe (VC ++compiler) in MATLAB the algorithm improves the scalability of image data processing platform (SPM,MATLAB),facilitates the completion of image processing flow on same platform,and enhances work efficiency.And by calling m subprogram in VC ++,the algorithm compiles be_ls core algorithm and fig procedures into an executable program that is independent of the programming environment.The single and executable exe can be run on a computer without installing MATLAB platform.The way of Standalone,which is not much rely on compiler environment,has been used by many professional software,but the reports about its application in MRI image processing have not yet been found.Experimental results show that through mixed calling of CV ++and MATLAB,it can not only achieve skull stripping effectively,but can also greatly short-ens the cycle of operation,so that improves the work efficiency.It has a positive reference and practical significance to promoting the develop-ment of the software compiler technology in the field.

药物性肝损害是最常见的药品不良反应之一,重者会导致黄疸、急性肝功能衰竭甚至死亡.其发病率逐年上升,已越来越受到人们的关注.影响因素包括患者因素(如年龄、性别、妊娠、营养不良和基础疾病等)、药物因素和环境因素.有效利用医院信息系统开展监测预警药物性肝损害的发生至关重要,因此建议国家和政府尽快建立药物性肝损害的监测系统.