Progressive alteration of dynamic functional connectivity patterns in subcortical ischemic vascular cognitive impairment patients.

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.

Effects of acute high-altitude exposure on working memory: A functional near-infrared spectroscopy study.

INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

A Lightweight Internet Sharing Scheme for Sectional Medical Images according to Existing Hospital Network Facilities and Basic Information Security Rules.

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.

Corrigendum: Aging Changes Effective Connectivity of Motor Networks During Motor Execution and Motor Imagery.

[This corrects the article DOI: 10.3389/fnagi.2019.00312.].

Disrupted Brain Structural Connectivity Network in Subcortical Ischemic Vascular Cognitive Impairment With No Dementia.

The alteration of the functional topological organization in subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND) patients has been illuminated by previous neuroimaging studies. However, in regard to the changes in the structural connectivity of brain networks, little has been reported. In this study, a total of 27 subjects, consisting of 13 SIVCIND patients, and 14 normal controls, were recruited. Each of the structural connectivity networks was constructed by diffusion tensor tractography. Subsequently, graph theory, and network-based statistics (NBS) were employed to analyze the whole-brain mean factional anisotropy matrix. After removing the factor of age, gender, and duration of formal education, the clustering coefficients (C p ) and global efficiency (E glob ) were significantly decreased and the mean path length (L p ) was significantly increased in SIVCIND patients compared with normal controls. Using the combination of four network topological parameters as the classification feature, a classification accuracy of 78% was obtained by leave-one-out cross-validation for all subjects with a sensitivity of 69% and a specificity of 86%. Moreover, we also found decreased structural connections in the SIVCIND patients, which mainly concerned fronto-occipital, fronto-subcortical, and tempo-occipital connections (NBS corrected, p < 0.01). Additionally, significantly altered nodal centralities were found in several brain regions of the SIVCIND patients, mainly located in the prefrontal, subcortical, and temporal cortices. These results suggest that cognitive impairment in SIVCIND patients is associated with disrupted topological organization and provide structural evidence for developing reliable biomarkers related to cognitive decline in SIVCIND.

Alterations of Brain Structural Network Connectivity in Type 2 Diabetes Mellitus Patients With Mild Cognitive Impairment.

Patients with type 2 diabetes mellitus (T2DM) are highly susceptible to developing dementia, especially for those with mild cognitive impairment (MCI), but its underlying cause is still unclear. This study aims to investigate the early detection of white matter structural network changes in T2DM patients with MCI and assess the relationship between cognitive impairment and structural network alterations in T2DM patients. In this study, we performed a battery of neuropsychological tests and diffusion tensor MRI in 30 T2MD-MCI patients, 30 T2DM patients with normal cognition (T2DM-NC) and 30 age-, sex-, and education-matched healthy control (HC) individuals. Cognitive performance exhibited obvious differences among the three groups. The structural network was significantly disrupted in both global and regional levels in T2DM patients. The T2DM-MCI group showed more severe impairment of global network efficiency, and lower nodal efficiency and fewer connections within multiple regions like the limbic system, basal ganglia, and several cortical structures. Moreover, a subnetwork impaired in T2DM-MCI patients was characterized by cortical-limbic fibers, and commissural fibers and pathways within the frontal, temporal, and occipital lobes. These altered global and nodal parameters were significantly correlated with cognitive function in T2DM-MCI patients. In particular, executive dysfunction and working memory impairment in T2DM-MCI patients correlated with nodal efficiency in the right opercular part and triangular part of the inferior frontal gyrus, which indicated that white matter disruption in these regions may act as potential biomarkers for T2DM-associated MCI detection. Our investigation provides a novel insight into the neuropathological effects of white matter network disruption on cognition impairments induced by T2DM.

Aging Changes Effective Connectivity of Motor Networks During Motor Execution and Motor Imagery.

Age-related neurodegenerative and neurochemical changes are considered to be the basis for the decline of motor function; however, the change of effective connections in cortical motor networks that come with aging remains unclear. Here, we investigated the age-related changes of the dynamic interaction between cortical motor regions. Twenty young subjects and 20 older subjects underwent both right hand motor execution (ME) and right hand motor imagery (MI) tasks by using functional magnetic resonance imaging. Conditional Granger causality analysis (CGCA) was used to compare young and older adults' effective connectivity among regions of the motor network during the tasks. The more effective connections among motor regions in older adults were found during ME; however, effective within-domain hemisphere connections were reduced, and the blood oxygenation level dependent (BOLD) signal was significantly delayed in older adults during MI. Supplementary motor area (SMA) had a significantly higher In+Out degree within the network during ME and MI in older adults. Our results revealed a dynamic interaction within the motor network altered with aging during ME and MI, which suggested that the interaction with cortical motor neurons caused by the mental task was more difficult with aging. The age-related effects on the motor cortical network provide a new insight into our understanding of neurodegeneration in older individuals.

Brain Functional Connectivity Plasticity Within and Beyond the Sensorimotor Network in Lower-Limb Amputees.

Cerebral neuroplasticity after amputation has been elucidated by functional neuroimaging. However, little is known concerning how brain network-level functional reorganization of the sensorimotor system evolves following lower-limb amputation. We studied 32 unilateral lower-limb amputees (LLAs) and 32 matched healthy controls (HCs) using resting-state functional magnetic resonance imaging (rs-fMRI). A regions of interest (ROI)-wise connectivity analysis was performed with ROIs in eight brain regions in the sensorimotor network to investigate intra-network changes, and seed-based whole-brain functional connectivity (FC) with a seed in the contralateral primary sensorimotor cortex (S1M1) was used to study the FC reorganization between the sensorimotor region (S1M1) and other parts of the brain in the LLAs. The ROI-wise connectivity analysis showed that the LLAs had decreased FC, mainly between the subcortical nuclei and the contralateral S1M1 (p < 0.05, FDR corrected). Seed-based whole-brain FC analysis revealed that brain regions with decreased FC with the contralateral S1M1 extended beyond the sensorimotor network to the prefrontal and visual cortices (p < 0.05, FDR corrected). Moreover, correlation analysis showed that decreased FC between the subcortical and the cortical regions in the sensorimotor network progressively increased in relation to the time since amputation. These findings indicated a cascade of cortical reorganization at a more extensive network level following lower-limb amputation, and also showed promise for the development of a possible neurobiological marker of changes in FC related to motor function recovery in LLAs.

Progressively Disrupted Brain Functional Connectivity Network in Subcortical Ischemic Vascular Cognitive Impairment Patients.

Cognitive impairment caused by subcortical ischemic vascular disease (SIVD) has been elucidated by many neuroimaging studies. However, little is known regarding the changes in brain functional connectivity networks in relation to the severity of cognitive impairment in SIVD. In the present study, 20 subcortical ischemic vascular cognitive impairment no dementia patients (SIVCIND) and 20 dementia patients (SIVaD) were enrolled; additionally, 19 normal controls were recruited. Each participant underwent a resting-state functional MRI scan. Whole-brain functional networks were analyzed with graph theory and network-based statistics (NBS) to study the functional organization of networks and find alterations in functional connectivity among brain regions. After adjustments for age, gender, and duration of formal education, there were significant group differences for two network functional organization indices, global efficiency and local efficiency, which decreased (NC > SIVCIND > SIVaD) as cognitive impairment worsened. Between-group differences in functional connectivity (NBS corrected, p < 0.01) mainly involved the orbitofrontal, parietal, and temporal cortices, as well as the basal ganglia. The brain connectivity network was progressively disrupted as cognitive impairment worsened, with an increased number of decreased connections between brain regions. We also observed more reductions in nodal efficiency in the prefrontal and temporal cortices for SIVaD than for SIVCIND. These findings indicated a progressively disrupted pattern of the brain functional connectivity network with increased cognitive impairment and showed promise for the development of reliable biomarkers of network metric changes related to cognitive impairment caused by SIVD.

Scalp acupuncture plus low-frequency rTMS promotes repair of brain white matter tracts in stroke patients: A DTI study.

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<0.05). Moreover, the increased fractional anisotropy values in the forceps minor in the experimental group were significantly positively correlated with the increased Fugl-Meyer assessment score. Our study concluded that based on routine rehabilitation treatment, scalp acupuncture plus low frequency rTMS can promote white matter tracts repair better than scalp acupuncture alone; the motor function improvement of the hemiplegic upper limb may be closely related to the rehabilitation of the forceps minor; the combination of scalp acupuncture and low frequency rTMS is expected to provide a more optimal rehabilitation protocol for stroke hemiplegic patients.

Disrupted structural and functional connectivity networks in ischemic stroke patients.

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.

An HTML5-Based Pure Website Solution for Rapidly Viewing and Processing Large-Scale 3D Medical Volume Reconstruction on Mobile Internet.

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 <12 s, and the response time of each behavior request for final high quality image remained at approximately 1 s, while the peak value of bandwidth was <50-KBps. Meanwhile, the FPS value for each client was ≥40. This solution could serve the users by rapidly accessing the application via one URL hyperlink without special software and hardware requirement in a diversified network environment and could be easily integrated into other telemedical systems seamlessly.

Dependency criterion based brain pathological age estimation of Alzheimer's disease patients with MR scans.

OBJECTIVES: Traditional brain age estimation methods are based on the 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 the accelerated brain aging. METHODS: This paper considers this deviation and obtains it by maximizing the correlation between the estimated brain age and the class label 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 the prior knowledge. Secondly, use the support vector regression 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 correlation 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. RESULTS: The experimental results showed that the separability of the samples was apparently improved. For normal control- Alzheimer's disease (NC-AD), normal control- mild cognition impairment (NC-MCI), and mild cognition impairment-Alzheimer's disease (MCI-AD), the average improvements were 0.164 (31.66%), 0.1284 (34.29%), and 0.0206 (7.1%), respectively. For NC-MCI-AD, the average improvement was 0.2002 (50.39%). The estimated brain pathological age could be not only more helpful for the classification of AD but also more precisely reflect the accelerated brain aging. CONCLUSION: In conclusion, this paper proposes a new kind of brain age-brain pathological age and offers an estimation method for it that can distinguish different states of AD, thereby better reflecting accelerated brain aging. Besides, the brain pathological age is most helpful for feature reduction, thereby simplifying the relevant classification algorithm.

Abnormal functional networks in resting-state of the sub-cortical chronic stroke patients with hemiplegia.

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.

[Detection algorithm of amyloid ß-protein deposition in magnetic resonance image based on pixel feature learning method].

Amyloid ß-protein (Aß) deposition is an important prevention and treatment target for Alzheimer's disease (AD), and early detection of Aß 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 Aß 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 Aß plaque deposition by elastic mapping. According to the experimental results, the proposed pixel features learning methods in this paper could extract and reflect Aß 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 Aß plaque deposition, thereby being helpful for improving classification accuracy of diagnosis of AD.

Cortical folding in post-traumatic stress disorder after motor vehicle accidents: Regional differences in gyrification.

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.

Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.

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.

Estimating the brain pathological age of Alzheimer's disease patients from MR image data based on the separability distance criterion.

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.

Detection of Aß plaque deposition in MR images based on pixel feature selection and class information in image level.

BACKGROUND: Amyloid ß-protein (Aß) 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 Aß 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 Aß plaque deposition without missing most of the Aß 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 Aß plaque deposition based on MR pixel feature selection algorithm is effective. The proposed algorithm can detect the information of the Aß 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 Aß plaque deposition on MR images and provide reference method for interested relevant researchers in public.

Conditional Granger Causality Analysis of Effective Connectivity during Motor Imagery and Motor Execution in Stroke Patients.

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.