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Inadequate oxygen availability may lead to impairment of neurocognitive functions. The aim of the present study was to investigate the effect of acute high-altitude exposure on the cerebral hemodynamic response and working memory.The same subjects performed working memory exercises with forward and backward digit span tasks both under normal oxygen conditions and in large simulated hypobaric hypoxia chambers, and a series of physiological parameters were evaluated. Functional near-infrared spectroscopy was used to measure cerebral blood flow changes in the dorsolateral prefrontal cortex (DLPFC) during the tasks.Compared with normoxic conditions, under hypoxic conditions, the heart rate and blood pressure increased, blood oxygen saturation decreased significantly, and the forward task had similar accuracy and response time, while the backward task had lower accuracy and longer response time. Neuroimaging analysis showed increased activation in the DLPFC during the forward task and deactivation during the backward task under hypobaric hypoxia conditions.Acute high-altitude exposure leads to physiological adaptations. The abnormal hemodynamic responses of the DLPFC to hypoxia at low pressure reveal the disruption of neurocognitive function by acute high-altitude exposure, which compromises complex cognitive functions, and provides a promising application for functional near infrared spectroscopy in the exploration of neural mechanisms in the brain during high-altitude exposure.

Alterations in the temporal evolution of brain states in the process of cognitive impairment aggravation due to subcortical ischemic vascular disease (SIVD) is not understood. The dynamic functional connectivity was investigated to identify the abnormal temporal properties of brain states associated with cognitive impairment caused by SIVD. Eighteen patients with subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND), 19 dementia patients (SIVaD) and 26 normal controls were enrolled. We found that the occupancy rate and mean lifetime of brain states were associated with cognitive performance. SIVCIND had a higher occupancy rate and longer mean lifetime in weakly connected states than normal controls. SIVaD had similar but more extensive changes in the temporal properties of brain states. In addition, switching from weakly connected states to more strongly connected states was more difficult in SIVCIND and SIVaD patients than in normal controls, especially in SIVaD patients. The results revealed that not only the transition to but also maintenance in strongly connected states became increasingly difficult when SIVD-related cognitive impairment progressed into a more severe stage.

Motor imagery refers to the psychological realization of movements without movement or muscle activity; it is a research hotspot in neurophysiology, neuroimaging, neurology, and psychology; and it is used as a neurological rehabilitation of brain-computer interface technology. The foundation is widely studied. The EEG signal has a millisecond time resolution, which can facilitate the acquisition of neural signal changes in the body movement process. This article has selected the finger and thumb index finger movements as the task of motion imaging. Under the guidance of image and video, the characteristics of the brain electrical shock patterns during the finger-to-finger movement, thumb and forefinger pairing and unfolding were studied. The brain electrical signals of 15 healthy subjects were collected during the experiment, and the signals were analyzed for source location and information flow network. The result of source location analysis indicated that the activation of brain regions was mainly in the contra lateral SMA region, PMC region, and M1 region. However, the subjects were right-handed, so the characteristics of contra lateral activation were not obvious in the right-handed motor imaging process. Imagine that the opposite side of the pinching process is more powerful than the imaginary finger and the activation range is wider. The results of dynamic information flow of EEG signals at different stages of motor imaging show obvious contra lateral activation characteristics. The DTF analysis of the left-hand motion imaging finger pair pinching and unfolding shows that the information flow mainly flows from the right side of the brain to the left in the imagination process, while the right-hand motion imaging process is opposite, and the information flow mainly flows from the left side of the brain to the right side. The left hand imagines that the finger-to-pinch process is simpler and the number of connections is smaller than the information flow of the imagined finger deployment process. The right hand imagines that the finger-to-kneading process looks more complex than the imagined finger-expanding process, and the number of connections is more. However, its more connections appear mainly in the occipital region of the visual stimulus, the information flow in the frontal and parietal lobes is simple, and the number of connections is reduced. This article explores the relationship among the different movement phases of hand motion imaging and the change of EEG signals. The results show that the brain oscillation modes are similar and different, and the finger deployment process is used as a follow-up of the finger-to-kneading process. The process of knowing should be relatively simple.

To study the clinical effects of scalp acupuncture plus low frequency rTMS in hemiplegic stroke patients. A total of 28 hemiplegic stroke patients were recruited and randomly assigned to the experimental group (scalp acupuncture low frequency rTMS routine rehabilitation treatment) or the control group (scalp acupuncture routine rehabilitation treatment). All patients received a diffusion tensor imaging examination on the day of admission and on the fourteenth day. Compared with pre-treatment, the upper limb motor function score and ability of daily life score increased significantly in the two groups, and motor function improvement was much greater in the experimental group. Fractional anisotropy values significantly increased in white matter tracts, such as the corticospinal tract, forceps minor, superior longitudinal fasciculus and uncinate fasciculus in the two groups. Compared with pre-treatment, the fractional anisotropy values increased and mean diffusion values decreased synchronously in the forceps minor, left inferior fronto-occipital fasciculus, left inferior longitudinal fasciculus, left superior longitudinal fasciculus and left uncinate fasciculus in the experimental group. Before and after treatment, there were no significant differences in the changes of fractional anisotropy values between the two groups, but the changes of the mean diffusion values in the experimental group were much greater than those in the control group in the left superior longitudinal fasciculus and the left uncinate fasciculus ($p

Background. With the outbreak of COVID-19, large-scale telemedicine applications can play an important role in the epidemic areas or less developed areas. However, the transmission of hundreds of megabytes of Sectional Medical Images (SMIs) from hospital’s Intranet to the Internet has the problems of efficiency, cost, and security. This article proposes a novel lightweight sharing scheme for permitting Internet users to quickly and safely access the SMIs from a hospital using an Internet computer anywhere but without relying on a virtual private network or another complex deployment. Methods. A four-level endpoint network penetration scheme based on the existing hospital network facilities and information security rules was proposed to realize the secure and lightweight sharing of SMIs over the Internet. A “Master-Slave” interaction to the interactive characteristics of multiplanar reconstruction and maximum/minimum/average intensity projection was designed to enhance the user experience. Finally, a prototype system was established. Results. When accessing SMIs with a data size ranging from 251.6 to 307.04 MB with 200 kBps client bandwidth (extreme test), the network response time to each interactive request remained at approximately 1 s, the original SMIs were kept in the hospital, and the deployment did not require a complex process; the imaging quality and interactive experience were recognized by radiologists. Conclusions. This solution could serve Internet medicine at a low cost and may promote the diversified development of mobile medical technology. Under the current COVID-19 epidemic situation, we expect that it could play a low-cost and high-efficiency role in remote emergency support.

Aim Structural and functional magnetic resonance imaging (MRI) studies have revealed evidence of brain abnormalities in post-traumatic stress disorder (PTSD) patients. Cortical complexity and local gyrification index (lGI) reflect potential biological processes associated with normal or abnormal cognitive functioning. In the current study, lGI was used to explore cortical folding in PTSD patients involved in motor vehicle accidents (MVA). Methods MRI brain scans were acquired from 18 PTSD patients who had suffered MVA at least 6 months previously and 18 healthy control subjects. All MRI images were obtained on a 3-T Siemens MRI machine and the cortical folding was analyzed using the workflow provided by software FreeSurfer. A general FreeSurfer’s general linear model was used in the group analysis. In addition, correlation analysis was performed between the average of lGI extracted from the significantly different areas and the data for the clinical scale. Results The PTSD patients had significantly greater Clinician-Administered PTSD Scale scores than the control group. The patients showed significantly reduced lGI in the left lateral orbitofrontal cortex, consistent with findings of previous volumetric studies on PTSD. But there were no significant correlations in the left lateral orbitofrontal cortex between Clinician-Administered PTSD Scale scores and lGI. Conclusion We suggest that abnormal gyrification in PTSD patients can be an important indicator of neurodevelopment deficits and may indeed be a biological marker for PTSD.

The aim of this study was to analyze brain functional connectivity and its relationship to cognition in patients with mild traumatic brain injury (mTBI).Twenty-five patients with mTBI and 25 healthy control subjects were studied using resting-state functional MRI (rs-fMRI). Amplitudes of low-frequency fluctuations (ALFFs) and functional connectivity (FC) were calculated and correlated with cognition.Compared with the normal control group, the mTBI patients showed a significant decrease in working memory index (WMI) and processing speed index (PSI), as well as significantly decreased ALFFs in the cingulate gyrus, the middle frontal gyrus and superior frontal gyrus. In contrast, the mTBI patients' ALFFs in the left middle occipital gyrus, the left precuneus, and lingual gyrus increased. Additionally, FC significantly decreased in the thalamus, caudate nucleus, and right hippocampus in the mTBI patients. Statistical analysis further showed a significant positive correlation between the ALFF in the cingulate gyrus and the WMI (R (2) = 0.423, P 0.05) and a significant positive correlation between the FC in the left thalamus and left middle frontal gyrus and the WMI (R (2) = 0.381, P 0.05).rs-fMRI can reveal the functional state of the brain in patients with mTBI. This finding differed from observations of the normal control group and was significantly associated with clinical cognitive dysfunction. Therefore, rs-fMRI offers an objective imaging modality for treatment planning and prognosis assessment in patients with mTBI.

Amyloid β-protein (Aβ) deposition is an important prevention and treatment target for Alzheimer's disease (AD), and early detection of Aβ deposition in the brain is the key to early diagnosis of AD. Magnetic resonance imaging (MRI) is the perfect imaging technology for the clinical diagnosis of AD, but it cannot display the plaque deposition directly. In this paper, based on two feature selection modes-filter and wrapper, chain-like agent genetic algorithm (CAGA), principal component analysis (PCA), support vector machine (SVM) and random forest (RF), we designed six kinds of feature learning classification algorithms to detect the information (distribution) of Aβ deposition through magnetic resonance image pixels selection. Firstly, we segmented the brain region from brain MR images. Secondly, we extracted the pixels in the segmented brain region as a feature vector (features) according to rows. Thirdly, we conducted feature learning on the extracted features, and obtained the final optimal feature subset by voting mechanism. Finally, using the final optimal selected features, we could find and mark the corresponding pixels on the MR images to show the information about Aβ plaque deposition by elastic mapping. According to the experimental results, the proposed pixel features learning methods in this paper could extract and reflect Aβ plaque deposition, and the best classification accuracy could be as high as 80%, thereby showing the effectiveness of the methods. The proposed methods can precisely detect the information of the Aβ plaque deposition, thereby being helpful for improving classification accuracy of diagnosis of AD.β 淀粉样蛋白（Aβ）沉积是阿尔茨海默症（AD）的重要防治靶点，在脑中及早发现 Aβ 蛋白沉积是 AD 早期诊断的关键。磁共振成像（MRI）是一种理想成像方式，但不能直接显示图像中存在的沉积信息。本文基于过滤式和封装式的选择模式引入链式智能体遗传算法（CAGA）、主成分分析（PCA）、支持向量机（SVM）和随机森林（RF），构建 6 种特征学习分类算法，通过像素特征优选来实现 Aβ 蛋白沉积信息（分布）的检测。首先，分割脑磁共振（MR）图像中的脑组织；然后提取脑组织中的像素值形成像素特征向量；接着设计特征学习分类算法对像素实现特征优选，并基于投票机制得到一组最终最优特征向量；最后采用弹性映射方法将最优像素特征向量映射到脑MR图像上，并标记出对应的像素点，从而显示出 Aβ 蛋白沉积的分布。实验结果表明，本文的像素特征学习方法可提取并显示 Aβ 蛋白沉积信息，最高分类准确率可达到 80% 以上，表明该方法是可行和有效的。本文从脑 MR 图像中检测的 Aβ 沉积信息将有助于提高基于 MR 的 AD 诊断准确率。.

Parkinson’s disease(PD)diagnosis based on speech data has been proved to be an effective way in recent years.There are still some problems on preprocessing samples,ensemble learning,and so on.The problems can further cause misleading of classifiers,unsatisfactory classification accuracy and stability.This paper proposed a new diagnosis algorithm of PD by combining multi-edit sample selection method and random forest.At the end of it,this paper presents a group of experiments carried out with the newest public datasets.Experimental results showed that this proposed algorithm realized the classification of the samples and the subjects of PD.Furthermore,it achieved average classification accuracy of 100% and obtained improvement of up to 29.44% compared to those provided by the subjects.This paper proposes a new speech diagnosis algorithm for PD based on instance selection;and the method algorithm has a higher and more stable classification accuracy,compared with the other algorithms.

To study the clinical effects of scalp acupuncture plus low frequency rTMS in hemiplegic stroke patients. A total of 28 hemiplegic stroke patients were recruited and randomly assigned to the experimental group (scalp acupuncture low frequency rTMS routine rehabilitation treatment) or the control group (scalp acupuncture routine rehabilitation treatment). All patients received a diffusion tensor imaging examination on the day of admission and on the fourteenth day. Compared with pre-treatment, the upper limb motor function score and ability of daily life score increased significantly in the two groups, and motor function improvement was much greater in the experimental group. Fractional anisotropy values significantly increased in white matter tracts, such as the corticospinal tract, forceps minor, superior longitudinal fasciculus and uncinate fasciculus in the two groups. Compared with pretreatment, the fractional anisotropy values increased and mean diffusion values decreased synchronously in the forceps minor, left inferior fronto-occipital fasciculus, left inferior longitudinal fasciculus, left superior longitudinal fasciculus and left uncinate fasciculus in the experimental group. Before and after treatment, there were no significant differences in the changes of fractional anisotropy values between the two groups, but the changes of the mean diffusion values in the experimental group were much greater than those in the control group in the left superior longitudinal fasciculus and the left uncinate fasciculus (p

This study aimed to propose a pure web-based solution to serve users to access large-scale 3D medical volume anywhere with good user experience and complete details. A novel solution of the Master-Slave interaction mode was proposed, which absorbed advantages of remote volume rendering and surface rendering. On server side, we designed a message-responding mechanism to listen to interactive requests from clients (Slave model) and to guide Master volume rendering. On client side, we used HTML5 to normalize user-interactive behaviors on Slave model and enhance the accuracy of behavior request and user-friendly experience. The results showed that more than four independent tasks (each with a data size of 249.4 MB) could be simultaneously carried out with a 100-KBps client bandwidth (extreme test); the first loading time was

Local lesions caused by stroke may result in extensive structural and functional reorganization in the brain. Previous studies of this phenomenon have focused on specific brain networks. Here, we aimed to discover abnormalities in whole-brain networks and to explore the decoupling between structural and functional connectivity in patients with stroke. Fifteen ischemic stroke patients and 23 normal controls (NCs) were recruited in this study. A graph theoretical analysis was employed to investigate the abnormal topological properties of structural and functional brain networks in patients with stroke. Both patients with stroke and NCs exhibited small-world organization in brain networks. However, compared to NCs, patients with stroke exhibited abnormal global properties characterized by a higher characteristic path length and lower global efficiency. Furthermore, patients with stroke showed altered nodal characteristics, primarily in certain motor- and cognition-related regions. Positive correlations between the nodal degree of the inferior parietal lobule and the Fugl-Meyer Assessment (FMA) score and between the nodal betweenness centrality of the posterior cingulate gyrus (PCG) and immediate recall were observed in patients with stroke. Most importantly, the strength of the structural-functional connectivity network coupling was decreased, and the coupling degree was related to the FMA score of patients, suggesting that decoupling may provide a novel biomarker for the assessment of motor impairment in patients with stroke. Thus, the topological organization of brain networks is altered in patients with stroke, and our results provide insights into the structural and functional organization of the brain after stroke from the viewpoint of network topology.

Functional neuroimaging studies have found intra-regional activity and inter-regional connectivity alterations in patients with post-traumatic stress disorder (PTSD). However, the results of these studies are based on group-level statistics and therefore it is unclear whether PTSD can be discriminated at single-subject level, for instance using the machine learning approach. Here, we proposed a novel framework to identify PTSD using multi-level measures derived from resting-state functional MRI (fMRI). Specifically, three levels of measures were extracted as classification features: (1) regional amplitude of low-frequency fluctuations (univariate feature), which represents local spontaneous synchronous neural activity; (2) temporal functional connectivity (bivariate feature), which represents the extent of similarity of local activity between two regions, and (3) spatial functional connectivity (multivariate feature), which represents the extent of similarity of temporal correlation maps between two regions. Our method was evaluated on 20 PTSD patients and 20 demographically matched healthy controls. The experimental results showed that the features of each level could successfully discriminate PTSD patients from healthy controls. Furthermore, the combination of multi-level features using multi-kernel learning can further improve the classification performance. Specifically, the classification accuracy obtained by the proposed framework was 92.5 %, which was an increase of at least 5 and 17.5 % from the two-level and single-level feature based methods, respectively. Particularly, the limbic structure and prefrontal cortex provided the most discriminant features for classification, consistent with results reported in previous studies. Together, this study demonstrated for the first time that patients with PTSD can be identified at the individual level using resting-state fMRI data. The promising classification results indicated that this method may provide a complementary approach for improving the clinical diagnosis of PTSD.

Objective To study the neural mechanism for the impact of negative emotional distraction on working memory in patients with posttraumatic stress disorder (PTSD) resulting from exposure to motor vehicle accidents. Methods Twenty PTSD patients and 20 healthy subjects were recruited. Event-related functional magnetic resonance imaging (fMRI) was used to investigate the effects of negative and neutral distractors on a delayed-response working memory task. All experiments were performed on a 3.0T MRI scanner, and the functional imaging data were analyzed using SPM8 software. Results The PTSD group showed poorer performance than the control group when the negative distractors were presented during the delay phase of working memory. The functional imaging indicated that, in the presence of negative relative to neutral distractors, the PTSD group showed higher activation in the emotion processing regions, including amygdala, precuneus and fusiform gyrus, but lower activation in the inferior frontal cortex, insula and left supramarginal gyrus than the control group. Conclusion Based on the results that activation in the PTSD patients in the presence of negative distractors increased in the emotion-related brain regions but decreased in the working memory-related brain regions, we may conclude that the neural basis of working memory is impaired by negative emotion in PTSD patients.

Background The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined. Methods In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation. Results Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms. Conclusions This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.

Traditional age estimation methods are based on the same idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to accelerated brain aging. This paper considers this deviation and searches for it by maximizing the separability distance value rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to prior knowledge. Secondly, use the support vector regression (SVR) as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the separability distance criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. The experimental results showed that the separability was apparently improved. For normal control-Alzheimer's disease (NC-AD), normal control-mild cognition impairment (NC-MCI), and MCI-AD, the average improvements were 0.178 (35.11%), 0.033 (14.47%), and 0.017 (39.53%), respectively. For NC-MCI-AD, the average improvement was 0.2287 (64.22%). The estimated brain pathological age could be not only more helpful to the classification of AD but also more precisely reflect accelerated brain aging. In conclusion, this paper offers a new method for brain age estimation that can distinguish different states of AD and can better reflect the extent of accelerated aging.

Background Amyloid β-protein (Aβ) plaque deposition is an important prevention and treatment target for Alzheimer’s disease (AD). As a noninvasive, nonradioactive and highly cost-effective clinical imaging method, magnetic resonance imaging (MRI) is the perfect imaging technology for the clinical diagnosis of AD, but it cannot display the plaque deposition directly. This paper resolves this problem based on pixel feature selection algorithms at the image level. Methods and results Firstly, the brain region was segmented from mouse model brain MR images. Secondly, the pixels in the segmented brain region were extracted as a feature vector (features). Thirdly, feature selection was conducted on the extracted features, and the optimal feature subset was obtained. Fourthly, the various optimal feature subsets were obtained by repeating the same processing above. Fifthly, based on the optimal feature subsets, the final optimal feature subset was obtained by voting mechanism. Finally, using the final optimal selected features, the corresponding pixels on the MR images could be found and marked to show the information about Aβ plaque deposition. The MR images and brain histological image slices of twenty-two model mice were used in the experiments. Four feature selection algorithms were used on the MR images and six kinds of classification experiments are conducted, thereby choosing a pixel feature selection algorithm for further study. The experimental results showed that by using the pixel features selected by the algorithms in this paper, the best classification accuracy between early AD and control slides could be as high as 80 %. The selected and marked MR pixels could show information of Aβ plaque deposition without missing most of the Aβ plaque deposition compared with brain histological slice images. The hit rate is over than 90 %. Conclusions According to the experimental results, the proposed detection algorithm of the Aβ plaque deposition based on MR pixel feature selection algorithm is effective. The proposed algorithm can detect the information of the Aβ plaque deposition on MR images and the information can be useful for improving the classification accuracy as assistant MR biomarker. Besides, these findings firstly show the feasibility of detection of the Aβ plaque deposition on MR images and provide reference method for interested relevant researchers in public.

The aim of this study is to identify the properties of the motor network and the default-mode network (DMN) of the sub-cortical chronic stroke patients, and to study the relationship between the network connectivity and the neurological scales of the stroke patients. Twenty-eight chronic stroke patients (28-77days post-stroke) and twenty-eight healthy control subjects (HCs) were recruited. Independent component analysis (ICA) was performed to obtain the motor network and the DMN. Two sample t-tests was used to compare the differences of the motor network and the DMN between the patients and HCs. Additionally, correlations between the network connectivity and the behavioral scores of the stroke patients were studied. Compared with the HCs, the motor network connectivity of the stroke patients was significantly increased in the contralesional superior parietal lobule, but decreased in ipsilesional M1. The DMN connectivity of the stroke patients was significantly increased in the contralesional middle frontal gyrus, but decreased in bilateral precuneus, ipsilesional supramarginal and angular gyrus. Moreover, the motor network connectivity of the contralesional superior parietal lobule was positively correlated with the Fugl-Meyer assessment (FMA) score of the stroke patients. Our results showed abnormal motor network and DMN during the resting-state of the stroke patients, suggesting that resting-state network connectivity could serve as biomarkers for future stroke studies. Brain-behavior relationships could be taken into account while evaluating stroke patients.

Objective Segmentation is a necessary step when creating realistic three-dimensional (3D) models. In order to build 3D models of whole body structures and have a wider lateral application, the thin sectional anatomical images of the Chinese Visible Human (CVH) dataset should be segmented. The more detailed structures are segmented to provide greater potential for wider application of the segmented images. Materials and methods All the images based on the CVH male and female dataset were segmented semi-automatically using PHOTOSHOP software. This research lasted about 7 years. Result In this study, 869 structures of CVH male and 860 structures of CVH female were semi-automatically segmented, and the formats for the segmented color-filled image data were PSD and PNG. In these segmented structures, nearly all skeletal muscles included muscle belly and tendon, and hollow organs included their organ walls and their lumen. Most nerve trunks, small arteries, lymph nodes, and lymph ducts were also segmented. Many surface-rendering and volume-rendering organ models were created using these segmented images. Conclusion The CVH male and female images represent the normal Asian population. After segmentation, the images can be reconstructed directly in 3D and greatly facilitate the biological modeling of physical and physiological information, a great help in improving medical and biological science in China.

Performing surgeries on the craniocervical junction presents a technical challenge for operating surgeons. Three-dimensional (3D) reconstruction and surgical simulation have improved the efficacy and success rate of surgeries. The aim of this study was to create a 3D, digitized visible model of the craniocervical junction region to help realize accurate simulation of craniocervical surgery on a graphic workstation. Transverse sectional anatomy data for the study were chosen from the first Chinese visible human. Manual axial segmentation of the skull base, cervical spine, cerebellum, vertebral artery, internal carotid artery, sigmoid sinus, internal jugular vein, brain stem, and spinal cord were carried out by using Photoshop software. The segmented structures were reconstructed in 3 dimensions with surface and volume rendering to accurately display 3D models spatially. In contrast to conventional 3D reconstruction techniques that are based on computed tomography and magnetic resonance imaging Digital Imaging and Communications in Medicine (DICOM) inputs and provide mostly osseous details, this technique can help to illustrate the surrounding soft tissue structure and provide a realistic surgical simulation. The reconstructed 3D model was successfully used in simulating complex procedures in the virtual environment, including the transoral approach, bone drillings, and clivus resection.

Aims.Motor imagery has emerged as a promising technique for the improvement of motor function following stroke, but the mechanism of functional network reorganization in patients during this process remains unclear. The aim of this study is to evaluate the cortical motor network patterns of effective connectivity in stroke patients.Methods.Ten stroke patients with right hand hemiplegia and ten normal control subjects were recruited. We applied conditional Granger causality analysis (CGCA) to explore and compare the functional connectivity between motor execution and motor imagery.Results.Compared with the normal controls, the patient group showed lower effective connectivity to the primary motor cortex (M1), the premotor cortex (PMC), and the supplementary motor area (SMA) in the damaged hemisphere but stronger effective connectivity to the ipsilesional PMC and M1 in the intact hemisphere during motor execution. There were tighter connections in the cortical motor network in the patients than in the controls during motor imagery, and the patients showed more effective connectivity in the intact hemisphere.Conclusions.The increase in effective connectivity suggests that motor imagery enhances core corticocortical interactions, promotes internal interaction in damaged hemispheres in stroke patients, and may facilitate recovery of motor function.

Structural and functional magnetic resonance imaging (MRI) studies have revealed evidence of brain abnormalities in post-traumatic stress disorder (PTSD) patients. Cortical complexity and local gyrification index (lGI) reflect potential biological processes associated with normal or abnormal cognitive functioning. In the current study, lGI was used to explore cortical folding in PTSD patients involved in motor vehicle accidents (MVA).MRI brain scans were acquired from 18 PTSD patients who had suffered MVA at least 6 months previously and 18 healthy control subjects. All MRI images were obtained on a 3-T Siemens MRI machine and the cortical folding was analyzed using the workflow provided by software FreeSurfer. A general FreeSurfer's general linear model was used in the group analysis. In addition, correlation analysis was performed between the average of lGI extracted from the significantly different areas and the data for the clinical scale.The PTSD patients had significantly greater Clinician-Administered PTSD Scale scores than the control group. The patients showed significantly reduced lGI in the left lateral orbitofrontal cortex, consistent with findings of previous volumetric studies on PTSD. But there were no significant correlations in the left lateral orbitofrontal cortex between Clinician-Administered PTSD Scale scores and lGI.We suggest that abnormal gyrification in PTSD patients can be an important indicator of neurodevelopment deficits and may indeed be a biological marker for PTSD.

Resting-state functional magnetic resonance imaging (fMRI) has been used to examine the brain mechanisms of stroke patients with hemiplegia, but the relationship between functional connectivity (FC) and treatment-induced motor function recovery has not yet been fully investigated. This study aimed to identify the brain FC changes in stroke patients and study the relationship between FC and motor function assessment using the resting-state fMRI. Seventeen stroke patients with hemiplegia and fifteen healthy control subjects (HCSs) were recruited in this study. We compared the FC between the ipsilesional primary motor cortex (M1) and the whole brain of the patients with the FC of the HCSs and studied the FC changes in the patients before and after conventional rehabilitation and motor imagery therapy. Additionally, correlations between the FC change and motor function of the patients were studied. Compared to the HCSs, the FC in the patient group was significantly increased between the ipsilesional M1 and the ipsilesional inferior parietal cortex, frontal gyrus, supplementary motor area (SMA), and contralesional angular and decreased between the ipsilesional M1 and bilateral M1. After the treatment, the FC between the ipsilesional M1 and contralesional M1 increased while the FC between the ipsilesional M1 and ipsilesional SMA and paracentral lobule decreased. A statistically significant correlation was found between the FC change in the bilateral M1 and the Fugl-Meyer assessment (FMA) score change. Our results revealed an abnormal motor network after stroke and suggested that the FC could serve as a biomarker of motor function recovery in stroke patients with hemiplegia.

The purpose of this study was to generate a computerized 3D reconstruction of the temporal bone and intratemporal structures. A plastination technique was used to obtain equidistant serial thin sections of 1.2 mm thickness and, on an SGI workstation, a Contour-Marching Cubes algorithm was selected to reconstruct the temporal bone and intratemporal structures in three dimensions. All reconstructed structures can be represented individually or jointly and rotated in any plane. Any diameter and angle of a structure can be conveniently measured. The capability of reconstructing individual and combined images of intratemporal structures, viewing them from all surgical angles, and accurately measuring their spatial relationships gives skull base and otologic surgeons important guidance. The reconstructed model can also be used for resident education, rehearsal of an unfamiliar surgery, and for developing a new surgical approach. Clin. Anat. 16:300–303, 2003. © 2003 Wiley-Liss, Inc.

An automatic method for cartilage segmentation using knee MRI images is described. Three binary classifiers with integral and partial pixel features are built using the Bayesian theorem to segment the femoral cartilage, tibial cartilage and patellar cartilage separately. First, an iterative procedure based on the feedback of the number of strong edges is designed to obtain an appropriate threshold for the Canny operator and to extract the bone-cartilage interface from MRI images. Second, the different edges are identified based on certain features, which allow for different cartilage to be distinguished synchronously. The cartilage is segmented preliminarily with minimum error Bayesian classifiers that have been previously trained. According to the cartilage edge and its anatomic location, the speed of segmentation is improved. Finally, morphological operations are used to improve the primary segmentation results. The cartilage edge is smooth in the automatic segmentation results and shows good consistency with manual segmentation results. The mean Dice similarity coefficient is 0.761.

Although alterations of topological organization have previously been reported in the brain functional network of Parkinson’s disease (PD) patients, the topological properties of the brain network in early-stage PD patients who received antiparkinson treatment are largely unknown. This study sought to determine the topological characteristics of the large-scale functional network in early-stage PD patients. First, 26early-stage PD patients (Hoehn and Yahr stage:1-2) and 30 age-matched normal controls were scanned using resting-state functional MRI. Subsequently, graph theoretical analysis was employed to investigate the abnormal topological configuration of the brain network in early-stage PD patients. We found that both the PD patient and control groups showed small-world properties in their functional brain networks. However, compared with the controls, the early-stage PD patients exhibited abnormal global properties, characterized by lower global efficiency. Moreover, the modular structure and the hub distribution were markedly altered in early-stage PD patients. Furthermore, PD patients exhibited increased nodal centrality, primarily in the bilateral pallidum, the inferior parietal lobule, and the medial superior frontal gyrus, and decreased nodal centrality in the caudate nucleus, the supplementary motor areas, the precentral gyrus, and the middle frontal gyrus. There were significant negative correlations between the Unified Parkinson Disease Rating Scale motor scores and nodal centralities of superior parietal gyrus. These results suggest that the topological organization of the brain functional network was altered in early-stage PD patients who received antiparkinson treatment, and we speculated that the antiparkinson treatment may affect the efficiency of the brain network to effectively relieve clinical symptoms of PD.

Designed methods for medical HRIs rapid accessing, presentation, and discussion on pure web browsers.Improved a tile-pyramid to adapt to the reading habits of users for HRIs accessing.Built a format conversion engine (FCE) to make DICOM browsing compatibility and response efficiency.Designed a XML behavior relationship to store and share users' behavior to keep online discussion.Created a HRIs sharing and electronic whiteboard system in a diversified network environment. There are various medical image sharing and electronic whiteboard systems available for diagnosis and discussion purposes. However, most of these systems ask clients to install special software tools or web plug-ins to support whiteboard discussion, special medical image format, and customized decoding algorithm of data transmission of HRIs (high-resolution images). This limits the accessibility of the software running on different devices and operating systems. In this paper, we propose a solution based on pure web pages for medical HRIs lossless sharing and e-whiteboard discussion, and have set up a medical HRI sharing and e-whiteboard system, which has four-layered design: (1) HRIs access layer: we improved an tile-pyramid model named unbalanced ratio pyramid structure (URPS), to rapidly share lossless HRIs and to adapt to the reading habits of users; (2) format conversion layer: we designed a format conversion engine (FCE) on server side to real time convert and cache DICOM tiles which clients requesting with window-level parameters, to make browsers compatible and keep response efficiency to server-client; (3) business logic layer: we built a XML behavior relationship storage structure to store and share users' behavior, to keep real time co-browsing and discussion between clients; (4) web-user-interface layer: AJAX technology and Raphael toolkit were used to combine HTML and JavaScript to build client RIA (rich Internet application), to meet clients' desktop-like interaction on any pure webpage. This system can be used to quickly browse lossless HRIs, and support discussing and co-browsing smoothly on any web browser in a diversified network environment. The proposal methods can provide a way to share HRIs safely, and may be used in the field of regional health, telemedicine and remote education at a low cost.

The aim of this study is to explore the white matter structure integrity in patients with mild traumatic brain injury (mTBI) using diffusion tensor imaging (DTI), and to analyze the relationship between the white matter structure integrity and cognitive impairment of patients with mTBI. Twenty-five patients with mTBI and 25 healthy control subjects were studied with conventional MR imaging and diffusion tensor imaging. Fractional anisotropy (FA) and mean diffusivity (MD) maps of patients with mTBI were calculated and compared, with these control maps using tract-based spatial statistics (TBSS). Significantly lower fractional anisotropy was found in patients in the uncinate fasciculus, superior longitudinal fasciculus, inferior longitudinal fasciculus, and internal capsule. Mean diffusivity was significantly elevated in the body of corpus callosum, uncinate fasciculus, superior longitudinal fasciculus, and internal capsule in the mTBI group compared with the control group (P

Objective To explore the neural mechanisms of negative emotion regulation in patients with post-traumatic stress disorder (PTSD). Methods Twenty PTSD patients and 20 healthy subjects were recruited. Event-related functional magnetic resonance imaging (fMRI) was used to investigate the modification of emotional responses to negative stimuli. Participants were required to regulate their emotional reactions according to the auditory regulation instructions via headphones, to maintain, enhance or diminish responses to negative stimuli during fMRI scans. Results The PTSD group showed poorer modification performance than the control group when diminishing responses to negative stimuli. On fMRI, the PTSD group showed decreased activation in the inferior frontal cortex, inferior parietal lobule, insula and putamen, and increased activation in posterior cingulate cortex and amygdala during up-regulation of negative emotion. Similar decreased activation regions were found during down-regulation of negative emotion, but no increased activation was found. Conclusion Trauma exposure might impair the ability to down-regulate negative emotion. The present findings will improve our understanding of the neural mechanisms of emotion regulation underlying PTSD.

The aim of this study is to investigate the white matter by the diffusion tensor imaging and the Chinese visible human dataset and to provide the 3D anatomical data of the corticospinal tract for the neurosurgical planning by studying the probabilistic maps and the reproducibility of the corticospinal tract. Diffusion tensor images and high-resolution T1-weighted images of 15 healthy volunteers were acquired; the DTI data were processed using DtiStudio and FSL software. The FA and color FA maps were compared with the sectional images of the Chinese visible human dataset. The probability maps of the corticospinal tract were generated as a quantitative measure of reproducibility for each voxel of the stereotaxic space. The fibers displayed by the diffusion tensor imaging were well consistent with the sectional images of the Chinese visible human dataset and the existing anatomical knowledge. The three-dimensional architecture of the white matter fibers could be clearly visualized on the diffusion tensor tractography. The diffusion tensor tractography can establish the 3D probability maps of the corticospinal tract, in which the degree of intersubject reproducibility of the corticospinal tract is consistent with the previous architectonic report. DTI is a reliable method of studying the fiber connectivity in human brain, but it is difficult to identify the tiny fibers. The probability maps are useful for evaluating and identifying the corticospinal tract in the DTI, providing anatomical information for the preoperative planning and improving the accuracy of surgical risk assessments preoperatively.

Background The major hindrance to multidetector CT imaging of the left extraperitoneal space (LES), and the detailed spatial relationships to its related spaces, is that there is no obvious density difference between them. Traditional gross anatomy and thick-slice sectional anatomy imagery are also insufficient to show the anatomic features of this narrow space in three-dimensions (3D). To overcome these obstacles, we used a new method to visualize the anatomic features of the LES and its spatial associations with related spaces, in random sections and in 3D. Methods In conjunction with Mimics® and Amira® software, we used thin-slice cross-sectional images of the upper abdomen, retrieved from the Chinese and American Visible Human dataset and the Chinese Virtual Human dataset, to display anatomic features of the LES and spatial relationships of the LES to its related spaces, especially the gastric bare area. The anatomic location of the LES was presented on 3D sections reconstructed from CVH2 images and CT images. Principal Findings What calls for special attention of our results is the LES consists of the left sub-diaphragmatic fat space and gastric bare area. The appearance of the fat pad at the cardiac notch contributes to converting the shape of the anteroexternal surface of the LES from triangular to trapezoidal. Moreover, the LES is adjacent to the lesser omentum and the hepatic bare area in the anterointernal and right rear direction, respectively. Conclusion The LES and its related spaces were imaged in 3D using visualization technique for the first time. This technique is a promising new method for exploring detailed communication relationships among other abdominal spaces, and will promote research on the dynamic extension of abdominal diseases, such as acute pancreatitis and intra-abdominal carcinomatosis.