[Automatic detection method of intracranial aneurysms on maximum intensity projection images based on SE-CaraNet].

Conventional maximum intensity projection (MIP) images tend to ignore some morphological features in the detection of intracranial aneurysms, resulting in missed detection and misdetection. To solve this problem, a new method for intracranial aneurysm detection based on omni-directional MIP image is proposed in this paper. Firstly, the three-dimensional magnetic resonance angiography (MRA) images were projected with the maximum density in all directions to obtain the MIP images. Then, the region of intracranial aneurysm was prepositioned by matching filter. Finally, the Squeeze and Excitation (SE) module was used to improve the CaraNet model. Excitation and the improved model were used to detect the predetermined location in the omni-directional MIP image to determine whether there was intracranial aneurysm. In this paper, 245 cases of images were collected to test the proposed method. The results showed that the accuracy and specificity of the proposed method could reach 93.75% and 93.86%, respectively, significantly improved the detection performance of intracranial aneurysms in MIP images.

CRANet: a comprehensive residual attention network for intracranial aneurysm image classification.

Rupture of intracranial aneurysm is the first cause of subarachnoid hemorrhage, second only to cerebral thrombosis and hypertensive cerebral hemorrhage, and the mortality rate is very high. MRI technology plays an irreplaceable role in the early detection and diagnosis of intracranial aneurysms and supports evaluating the size and structure of aneurysms. The increase in many aneurysm images, may be a massive workload for the doctors, which is likely to produce a wrong diagnosis. Therefore, we proposed a simple and effective comprehensive residual attention network (CRANet) to improve the accuracy of aneurysm detection, using a residual network to extract the features of an aneurysm. Many experiments have shown that the proposed CRANet model could detect aneurysms effectively. In addition, on the test set, the accuracy and recall rates reached 97.81% and 94%, which significantly improved the detection rate of aneurysms.

Application of 1H-MRS in end-stage renal disease with depression.

BACKGROUND: To investigate the metabolite changes in the frontal lobe of the end-stage renal disease (ESRD) patients with depression using proton magnetic resonance spectroscopy (1H-MRS). METHODS: All subjects were divided into three groups: ESRD patients with depression (30 cases), ESRD patients without depression (27 cases) and 32 normal subjects. ESRD with depression patients were further divided into two groups according to the severity of depression: 14 cases of ESRD with severe depression group (Hamilton Depression Rating Scale (HAMD) score ≥ 35) and 16 cases of ESRD with mild to moderate depression group (20 ≤ HAMD score<35). 1H-MRS was used in brain regions of all subjects to measure N-acetylaspartate/creatine (NAA/Cr), choline-containing compounds/creatine (Cho/Cr) and myo-inositol/creatine (MI/Cr) ratios of the frontal lobe. Correlations between the metabolite ratio and HAMD score as well as clinical finding were confirmed, respectively. RESULTS: ESRD patients with depression showed lower NAA/Cr ratio and higher Cho/Cr ratio compared with ESRD patients without depression and normal subjects. NAA/Cr ratio was negatively correlated with the HAMD score. Cho/Cr ratio was positively correlated with the HAMD score. There were positive correlations between NAA/Cr ratio and blood urea notrogen (BUN) as well as creatinine (CRE) concentration, respectively. There was a negative correlation between Cho/Cr ratio and sodium concentration. The Cho/Cr ratio was positively correlated with the potassium concentration. CONCLUSIONS: MR spectroscopy identified some metabolite changes in ESRD patients with depression.

[An algorithm for three-dimensional plumonary parenchymal segmentation by integrating surfacelet transform with pulse coupled neural network].

In order to overcome the difficulty in lung parenchymal segmentation due to the factors such as lung disease and bronchial interference, a segmentation algorithm for three-dimensional lung parenchymal is presented based on the integration of surfacelet transform and pulse coupled neural network (PCNN). First, the three-dimensional computed tomography of lungs is decomposed into surfacelet transform domain to obtain multi-scale and multi-directional sub-band information. The edge features are then enhanced by filtering sub-band coefficients using local modified Laplacian operator. Second, surfacelet inverse transform is implemented and the reconstructed image is fed back to the input of PCNN. Finally, iteration process of the PCNN is carried out to obtain final segmentation result. The proposed algorithm is validated on the samples of public dataset. The experimental results demonstrate that the proposed algorithm has superior performance over that of the three-dimensional surfacelet transform edge detection algorithm, the three-dimensional region growing algorithm, and the three-dimensional U-NET algorithm. It can effectively suppress the interference coming from lung lesions and bronchial, and obtain a complete structure of lung parenchyma.

Comparative study of striatum GABA concentrations and magnetic resonance spectroscopic imaging in Parkinson's disease monkeys.

BACKGROUND: Parkinson's disease is a progressive degenerative nervous system disease. Recent studies have shown that secondary changes in the GABA system play directly affect the pathogenesis of PD. There is still much debate about GABA concentrations because currently, GABA concentrations in the brain tissue are obtained indirectly by measuring its concentration in the blood and cerebrospinal fluid. These results are unreliable. Magnetic resonance spectroscopy (MRS) is the only noninvasive method for evaluating the concentration of metabolites in living brain tissue and has been widely applied in research and clinical practice. In addition, combining MEGA-PRESS technology with LCModel software for quantitative GABA measurements is largely recognized. At present, the PD monkeys model in primates has been increasingly proficient. Primates are more similar to humans in terms of brain structure and function than other animals. However, 3.0 T MRS studies involving the PD monkey model to measure metabolites in living subjects with PD are still rare. The study was performed at 3.0 T MRI with control monkeys and PD monkeys that were injected methyl-phenyl-tetrahydropyridine (MPTP) in one side of common carotid artery before and 3 months after successful model establishment to measure GABA concentrations in the bilateral striatum. Behavioral observations were performed for all animals, and the behavioral score was recorded. After 3 months, the GABA concentration in the bilateral striatum was measured in both groups by high-performance liquid chromatography (HPLC). The data obtained from magnetic resonance spectroscopy (MRS) were compared with the actual measured GABA concentrations in tissues isolated from the corresponding regions, and their correlations with the behavior score were analyzed. The research objectives are to investigate the changes of γ-aminobutyric acid (GABA) concentration in the bilateral striatum of monkeys with Parkinson's disease (PD) and the value of quantitatively measuring its concentration by noninvasive 3.0 T spectroscopy. RESULTS: (1) The MRS results showed that the GABA concentration in the injured side of the striatum of the PD monkeys was higher than in the contralateral side, but the difference was not statistically significant (P = 0.154). Compared with that the blank control group, the GABA concentration in the striatum of the PD monkeys increased, but there was no difference between the groups (P = 0.381; P = 0.425). (2) The GABA concentration that determined from the isolated specimens by HPLC in the injured side of the striatum of the PD monkeys was significantly higher than that in the contralateral side (P < 0.01). Compared with the blank control group, the PD monkeys had higher GABA concentrations in both sides of the striatum, and there was a significant difference in the lesion side (P = 0.004), while there was a non-significant difference in the contralateral side (P = 0.475). (3) The mean GABA concentration in the injured striatum of PD monkeys determined by MRS was not significantly correlated with the behavioral score (r = 0.146, P = 0.688). The mean GABA concentration in the injured striatum determined from the isolated specimens was positively correlated with the behavioral score in the same period (r = 0.444, P = 0.038). CONCLUSION: The GABA concentration in the injured striatum of PD monkeys is increased and positively correlated with behavioral changes. Validity of noninvasive 3.0 T MRS to detect PD neurotransmitter changes is limited.

Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis.

Dynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capture either temporal or spatial properties, such as mean and global efficiency, neglecting the significant information embedded in the fusion of spatial and temporal attributes. In addition, dynamic functional connectivity suffers from the problem of temporal mismatch, i.e., the functional connectivity of different subjects at the same time point cannot be matched. To address these problems, this article introduces a novel feature extraction framework grounded in two-directional two-dimensional principal component analysis. This framework is designed to extract features that integrate both spatial and temporal properties of dynamic functional connectivity. Additionally, we propose to use Fourier transform to extract temporal-invariance properties contained in dynamic functional connectivity. Experimental findings underscore the superior performance of features extracted by this framework in classification experiments compared to features capturing individual properties.

Preoperative MRI-based radiomic nomogram for distinguishing solitary fibrous tumor from angiomatous meningioma: a multicenter study.

Purpose: This study evaluates the efficacy of radiomics-based machine learning methodologies in differentiating solitary fibrous tumor (SFT) from angiomatous meningioma (AM). Materials and methods: A retrospective analysis was conducted on 171 pathologically confirmed cases (94 SFT and 77 AM) spanning from January 2009 to September 2020 across four institutions. The study comprised a training set (n=137) and a validation set (n=34). All patients underwent contrast-enhanced T1-weighted (CE-T1WI) and T2-weighted(T2WI) MRI scans, from which 1166 radiomics features were extracted. Subsequently, seventeen features were selected through minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was employed to assess the independence of these features as predictors. A clinical model, established via both univariate and multivariate logistic regression based on MRI morphological features, was integrated with the optimal radiomics model to formulate a radiomics nomogram. The performance of the models was assessed utilizing the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), and negative predictive value (NPV). Results: The radiomics nomogram demonstrated exceptional discriminative performance in the validation set, achieving an AUC of 0.989. This outperformance was evident when compared to both the radiomics algorithm (AUC= 0.968) and the clinical model (AUC = 0.911) in the same validation sets. Notably, the radiomics nomogram exhibited impressive values for ACC, SEN, and SPE at 97.1%, 93.3%, and 100%, respectively, in the validation set. Conclusions: The machine learning-based radiomic nomogram proves to be highly effective in distinguishing between SFT and AM.

In vitro and in vivo degradation, biocompatibility and bone repair performance of strontium-doped montmorillonite coating on Mg-Ca alloy.

Poor bone growth remains a challenge for degradable bone implants. Montmorillonite and strontium were selected as the carrier and bone growth promoting elements to prepare strontium-doped montmorillonite coating on Mg-Ca alloy. The surface morphology and composition were characterized by SEM, EDS, XPS, FT-IR and XRD. The hydrogen evolution experiment and electrochemical test results showed that the Mg-Ca alloy coated with Sr-MMT coating possessed optimal corrosion resistance performance. Furthermore, in vitro studies on cell activity, ALP activity, and cell morphology confirmed that Sr-MMT coating had satisfactory biocompatibility, which can significantly avail the proliferation, differentiation, and adhesion of osteoblasts. Moreover, the results of the 90-day implantation experiment in rats indicated that, the preparation of Sr-MMT coating effectively advanced the biocompatibility and bone repair performance of Mg-Ca alloy. In addition, The Osteogenic ability of Sr-MMT coating may be due to the combined effect of the precipitation of Si4+ and Sr2+ in Sr-MMT coating and the dissolution of Mg2+ and Ca2+ during the degradation of Mg-Ca alloy. By using coating technology, this study provides a late-model strategy for biodegradable Mg alloys with good corrosion resistance, biocompatibility. This new material will bring more possibilities in bone repair.

Multimodal transformer graph convolution attention isomorphism network (MTCGAIN): a novel deep network for detection of insomnia disorder.

Background: In clinic, the subjectivity of diagnosing insomnia disorder (ID) often leads to misdiagnosis or missed diagnosis, as ID may have the same symptoms as those of other health problems. Methods: A novel deep network, the multimodal transformer graph convolution attention isomorphism network (MTGCAIN) is proposed in this study. In this network, graph convolution attention (GCA) is first employed to extract the graph features of brain connectivity and achieve good spatial interpretability. Second, the MTGCAIN comprehensively utilizes multiple brain network atlases and a multimodal transformer (MT) to facilitate coded information exchange between the atlases. In this way, MTGCAIN can be used to more effectively identify biomarkers and arrive at accurate diagnoses. Results: The experimental results demonstrated that more accurate and objective diagnosis of ID can be achieved using the MTGCAIN. According to fivefold cross-validation, the accuracy reached 81.29% and the area under the receiver operating characteristic curve (AUC) reached 0.8760. A total of nine brain regions were detected as abnormal, namely right supplementary motor area (SMA.R), right temporal pole: superior temporal gyrus (TPOsup.R), left temporal pole: superior temporal gyrus (TPOsup.L), right superior frontal gyrus, dorsolateral (SFGdor.R), right middle temporal gyrus (MTG.R), left middle temporal gyrus (MTG.L), right inferior temporal gyrus (ITG.R), right median cingulate and paracingulate gyri (DCG.R), left median cingulate and paracingulate gyri (DCG.L). Conclusions: The brain regions in the default mode network (DMN) of patients with ID show significant impairment (occupies four-ninths). In addition, the functional connectivity (FC) between the right middle occipital gyrus and inferior temporal gyrus (ITG) has an obvious correlation with comorbid anxiety (P=0.008) and depression (P=0.005) among patients with ID.

Study of Typical Ruptured and Unruptured Intracranial Aneurysms Based on Fluid-Structure Interaction.

BACKGROUND: Most intracranial aneurysms (IAs) will be abnormal bulges on the walls of intracranial arteries that result from the dynamic interaction of geometric morphology, hemodynamics, and pathophysiology. Hemodynamics plays a key role in the origin, development, and rupture of IAs. In the past, hemodynamic studies of IAs were mostly based on the rigid wall hypothesis of computational fluid dynamics, and the influence of arterial wall deformation was ignored. We used fluid-structure interaction (FSI) to study the features of ruptured aneurysms, because it can solve this problem very well and the simulation will be more realistic. METHODS: A total of 12 IAs, 8 ruptured and 4 unruptured, at the middle cerebral artery bifurcation were studied using FSI to better identify the characteristics of ruptured IAs. We studied the differences in the hemodynamic parameters, including the flow pattern, wall shear stress (WSS), oscillatory shear index (OSI), and displacement and deformation of the arterial wall. RESULTS: Ruptured IAs had a larger low WSS area and more complex, concentrated, and unstable flow. Also, the OSI was higher. In addition, the displacement deformation area at the ruptured IA was more concentrated and larger. CONCLUSIONS: A large aspect ratio; a large height/width ratio; complex, unstable, and concentrated flow patterns with small impact areas; a large low WSS region; large WSS fluctuation, high OSI; and large displacement of the aneurysm dome could be risk factors associated with aneurysm rupture. If similar cases are encountered when simulation is used in the clinic, priority should be given to diagnosis and treatment.

An Epileptic Seizure Prediction Method Based on CBAM-3D CNN-LSTM Model.

Epilepsy as a common disease of the nervous system, with high incidence, sudden and recurrent characteristics. Therefore, timely prediction of seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients. Epilepsy seizures is the result of temporal and spatial evolution, Existing deep learning methods often ignore its spatial features, in order to make better use of the temporal and spatial characteristics of epileptic EEG signals. We propose a CBAM-3D CNN-LSTM model to predict epilepsy seizures. First, we apply short-time Fourier transform(STFT) to preprocess EEG signals. Secondly, the 3D CNN model was used to extract the features of preictal stage and interictal stage from the preprocessed signals. Thirdly, Bi-LSTM is connected to 3D CNN for classification. Finally CBAM is introduced into the model. Different attention is given to the data channel and space to extract key information, so that the model can accurately extract interictal and pre-ictal features. Our proposed approach achieved an accuracy of 97.95%, a sensitivity of 98.40%, and a false alarm rate of 0.017 h-1 on 11 patients from the public CHB-MIT scalp EEG dataset. Clinical and Translational Impact Statement-Timely prediction of epileptic seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients.

Antibacterial Activity and Biocompatibility of Ag-Montmorillonite/Chitosan Colloidal Dressing in a Skin Infection Rat Model: An In Vitro and In Vivo Study.

(1) Background: Traditional dressings can only superficially cover the wound, they have widespread issues with inadequate bacterial isolation and liquid absorption, and it is simple to inflict secondary wound injury when changing dressings. Therefore, it is crucial for wound healing to develop a new kind of antimicrobial colloidal dressing with good antibacterial, hygroscopic, and biocompatible qualities. (2) Methods: Ag-montmorillonite/chitosan (Ag-MMT/CS) colloid, a new type of antibacterial material, was prepared from two eco-friendly materials-namely, montmorillonite and chitosan-as auxiliary materials, wherein these materials were mixed with the natural metal Ag, which is an antibacterial agent. The optimum preparation technology was explored, and Ag-MMT/CS was characterized. Next, Staphylococcus aureus, which is a common skin infection bacterium, was considered as the experimental strain, and the in vitro antibacterial activity and cytocompatibility of the Ag-MMT/CS colloid were investigated through various experiments. Subsequently, a rat skin infection model was established to explore the in vivo antibacterial effect. (3) Results: In vitro studies revealed that the Ag-MMT/CS colloid had a good antibacterial effect on S. aureus, with an inhibition zone diameter of 18 mm and an antibacterial rate of 99.18%. After co-culture with cells for 24 h and 72 h, the cell survival rates were 88% and 94%, respectively. The cells showed normal growth and proliferation, and no evident dead cells were observed under the laser confocal microscope. After applying the colloid to the rat skin infection model, the Ag-MMT/CS treatment group exhibited faster wound healing and better local exudation and absorption in the wound than the control group, suggesting that the Ag-MMT/CS colloid exhibited a better antibacterial effect on the S. aureus. (4) Conclusions: Ag+, chitosan, and MMT present in the Ag-MMT/CS colloid dressing exert synergistic effects, and it has good antibacterial effects, cytocompatibility, and hygroscopicity, indicating that this colloid has the potential to become a next-generation clinical antibacterial dressing.

Extracting transition features among brain states based on coarse-grained similarity measurement for autism spectrum disorder analysis.

BACKGROUND: The abnormal brain functional connectivity (FC) of patients with mental diseases is closely linked to the transition features among brain states. However, the current research on state transition will produce certain division deviations in the measurement method of state division, and also ignore the transition features among multiple states that contain more abundant information for analyzing brain diseases. PURPOSE: To investigate the potential of the proposed method based on coarse-grained similarity measurement to solve the problem of state division, and consider the transition features among multiple states to analyze the FC abnormalities of autism spectrum disorder (ASD) patients. METHODS: We used resting-state functional magnetic resonance imaging to examine 45 ASD and 47 healthy controls (HC). The FC between brain regions was calculated by the sliding window and correlation algorithm, and a novel coarse-grained similarity measure method was used to cluster the FC networks into five states, and then extract the features both of the state itself and the transition features among multiple states for analysis and diagnosis. RESULTS: (1) The state as divided by the coarse-grained measurement method improves the diagnostic performance of individuals with ASD compared with previous methods. (2) The transition features among multiple states can provide complementary information to the features of the state itself in the ASD analysis and diagnosis. (3) ASD individuals have different brain state transitions than HC. Specifically, the abnormalities in intra- and inter-network connectivity of ASD patients mainly occur in the default mode network, the visual network, and the cerebellum. CONCLUSIONS: Such results demonstrate that our approach with new measurements and new features is effective and promising in brain state analysis and ASD diagnosis.

Multi-classifier fusion based on belief-value for the diagnosis of autism spectrum disorder.

Introduction: Autism Spectrum Disorder (ASD) has a significant impact on the health of patients, and early diagnosis and treatment are essential to improve their quality of life. Machine learning methods, including multi-classifier fusion, have been widely used for disease diagnosis and prediction with remarkable results. However, current multi-classifier fusion methods lack the ability to measure the belief level of different samples and effectively fuse them jointly. Methods: To address these issues, a multi-classifier fusion classification framework based on belief-value for ASD diagnosis is proposed in this paper. The belief-value measures the belief level of different samples based on distance information (the output distance of the classifier) and local density information (the weight of the nearest neighbor samples on the test samples), which is more representative than using a single type of information. Then, the complementary relationships between belief-values are captured via a multilayer perceptron (MLP) network for effective fusion of belief-values. Results: The experimental results demonstrate that the proposed classification framework achieves better performance than a single classifier and confirm that the fusion method used can effectively fuse complementary relationships to achieve accurate diagnosis. Discussion: Furthermore, the effectiveness of our method has only been validated in the diagnosis of ASD. For future work, we plan to extend this method to the diagnosis of other neuropsychiatric disorders.

Investigation of the correlation between brain functional connectivity and ESRD based on low-order and high-order feature analysis of rs-fMRI.

BACKGROUND: The lack of analysis of brain networks in individuals with end-stage renal disease (ESRD) is an obstacle to detecting and preventing neurological complications of ESRD. PURPOSE: This study aims to explore the correlation between brain activity and ESRD based on a quantitative analysis of the dynamic functional connectivity (dFC) of brain networks. It provides insights into differences in brain functional connectivity between healthy individuals and ESRD patients and aims to identify the brain activities and regions most relevant to ESRD. METHODS: Differences in brain functional connectivity between healthy individuals and ESRD patients were analyzed and quantitatively evaluated in this study. Blood oxygen level-dependent (BOLD) signals obtained through resting-state functional magnetic resonance imaging (rs-fMRI) were used as information carriers. First, a connectivity matrix of dFC was constructed for each subject using Pearson correlation. Then a high-order connectivity matrix was built by applying the "correlation's correlation" method. Second, sparsification of the high-order connectivity matrix was performed using the graphical least absolute shrinkage and selection operator (gLASSO) model. The discriminative features of the sparse connectivity matrix were extracted and sifted using central moments and t-tests, respectively. Finally, feature classification was conducted using a support vector machine (SVM). RESULTS: The experiment showed that functional connectivity was reduced to some degree in certain brain regions of ESRD patients. The sensorimotor, visual, and cerebellum subnetworks had the highest numbers of abnormal functional connectivities. It is inferred that these three subnetworks most likely have a direct relationship to ESRD. CONCLUSIONS: The low-order and high-order dFC features can identify the positions where brain damage occurs in ESRD patients. In contrast to healthy individuals, the damaged brain regions and the disruption of functional connectivity in ESRD patients were not limited to specific regions. This indicates that ESRD has a severe impact on brain function. Abnormal functional connectivity was mainly associated with the three functional brain regions responsible for visual processing, emotional, and motor control. The findings presented here have the potential for use in the detection, prevention, and prognostic evaluation of ESRD.

Comparative analysis of U-Net and TLMDB GAN for the cardiovascular segmentation of the ventricles in the heart.

OBJECTIVE: Magnetic Resonance Image (MRI) is an important imaging modality for diagnosing heart disease and analyzing heart function. The size and shape of the ventricle are important parameters for judging whether the heart is normal, and the ventricles in the MRI image is effectively segmented It is the key to obtain the ventricle size, shape and other parameters. Accurate segmentation of the entricle is the fundamental guarantee for the evaluation of cardiac function. However, in the heart image, the contrast between the ventricle area and the background area is not obvious, the boundary is blurred, and there is noise in most of the images. The accurate segmentation of the ventricle becomes a challenging problem. METHODS: We performed scanning of short-axis cardiac MR image sequences based on 33 subjects. Each subject has 8 to 15 sequences, each pertaining to a 20-frame sequence. Based on the U-Net neural network structure, the high-resolution information directly transferred from the encoder to the same-height decoder through the connection operation can provide more refined features for segmentation, such as gradients. The MRI left ventricular image segmentation method based on transfer learning and multi-scale discriminant Generative Adversarial Network (TLMDB GAN) solves the problem of insufficient ventricular image data. RESULTS: According to the experimental results of TLMDB GAN and U-Net network on the data set, the Dice coefficients of TLMDB GAN segmentation of the inner cardiac wall and outer cardiac wall of the ventricle are 0.9399 and 0.9697, respectively, which are 0.01 higher than other methods. The Dice coefficients of U-Net segmentation of the inner cardiac wall and outer cardiac wall of the ventricle are 0.8829 and 0.9292, respectively; CONCLUSION: The experimental results show that the TLMDB GAN based on transfer learning and multi-scale discrimination significantly improves the segmentation accuracy when compared with the U-Net segmentation model.

DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images.

Segmentation of intracranial aneurysm images acquired using magnetic resonance angiography (MRA) is essential for medical auxiliary treatments, which can effectively prevent subarachnoid hemorrhages. This paper proposes an image segmentation model based on a dense convolutional attention U-Net, which fuses deep and rich semantic information with shallow-detail information for adaptive and accurate segmentation of MRA-acquired aneurysm images with large size differences. The U-Net model serves as a backbone, combining dense block and convolution block attention module (CBAM). The dense block is composed of a batch normalization layer, an randomly rectified linear unit activation function, and a convolutional layer, for mitigation of vanishing gradients, for multiplexing of aneurysm features, and for improving the network training efficiency. The CBAM is composed of a channel attention module and a spatial attention module, improving the segmentation performance of feature discrimination and enhancing the acquisition of key feature information. Owing to the large variation of aneurysm sizes, multi-scale fusion is performed during up-sampling, for adaptive segmentation of MRA-acquired aneurysm images. The model was tested on the MICCAI 2020 ADAM dataset, and its generalizability was validated on the clinical aneurysm dataset (aneurysm sizes: < 3 mm, 3-7 mm, and > 7 mm) supplied by the Affiliated Hospital of Qingdao University. A good clinical application segmentation performance was demonstrated.

Identifying depression disorder using multi-view high-order brain function network derived from electroencephalography signal.

Brain function networks (BFN) are widely used in the diagnosis of electroencephalography (EEG)-based major depressive disorder (MDD). Typically, a BFN is constructed by calculating the functional connectivity (FC) between each pair of channels. However, it ignores high-order relationships (e.g., relationships among multiple channels), making it a low-order network. To address this issue, a novel classification framework, based on matrix variate normal distribution (MVND), is proposed in this study. The framework can simultaneously generate high-and low-order BFN and has a distinct mathematical interpretation. Specifically, the entire time series is first divided into multiple epochs. For each epoch, a BFN is constructed by calculating the phase lag index (PLI) between different EEG channels. The BFNs are then used as samples, maximizing the likelihood of MVND to simultaneously estimate its low-order BFN (Lo-BFN) and high-order BFN (Ho-BFN). In addition, to solve the problem of the excessively high dimensionality of Ho-BFN, Kronecker product decomposition is used for dimensionality reduction while retaining the original high-order information. The experimental results verified the effectiveness of Ho-BFN for MDD diagnosis in 24 patients and 24 normal controls. We further investigated the selected discriminative Lo-BFN and Ho-BFN features and revealed that those extracted from different networks can provide complementary information, which is beneficial for MDD diagnosis.

High-order brain functional network for electroencephalography-based diagnosis of major depressive disorder.

Brain functional network (BFN) based on electroencephalography (EEG) has been widely used to diagnose brain diseases, such as major depressive disorder (MDD). However, most existing BFNs only consider the correlation between two channels, ignoring the high-level interaction among multiple channels that contain more rich information for diagnosing brain diseases. In such a sense, the BFN is called low-order BFN (LO-BFN). In order to fully explore the high-level interactive information among multiple channels of the EEG signals, a scheme for constructing a high-order BFN (HO-BFN) based on the "correlation's correlation" strategy is proposed in this paper. Specifically, the entire EEG time series is firstly divided into multiple epochs by sliding window. For each epoch, the short-term correlation between channels is calculated to construct a LO-BFN. The correlation time series of all channel pairs are formulated by these LO-BFNs obtained from all epochs to describe the dynamic change of short-term correlation along the time. To construct HO-BFN, we cluster all correlation time series to avoid the problems caused by high dimensionality, and the correlation of the average correlation time series from different clusters is calculated to reflect the high-order correlation among multiple channels. Experimental results demonstrate the efficiency of the proposed HO-BFN in MDD identification, and its integration with the LO-BFN can further improve the recognition rate.

Fiber connectivity density mapping in end-stage renal disease patients: a preliminary study.

Abnormal brain structural connectivity of end-stage renal disease(ESRD) is associated with cognitive impairment. However, the characteristics of cortical structural connectivity have not been investigated in ESRD patients. Here, we study structural connectivity of the entire cerebral cortex using a fiber connectivity density(FiCD) mapping method derived from diffusion tensor imaging(DTI) data of 25 ESRD patients and 20 healthy controls, and between-group differences were compared in a vertexwise manner. We also investigated the associations between these abnormal cortical connectivities and the clinical variables using Pearson correlation analysis and multifactor linear regression analysis. Our results demonstrated that the mean global FiCD value was significantly decreased in ESRD patients. Notably, FiCD values were significantly changed(decreased or increased) in certain cortical regions, which mainly involved the bilateral dorsolateral prefrontal cortex(DLPFC), inferior parietal cortex, lateral temporal cortex and middle occipital cortex. In ESRD patients, we found a trend of negative correlation between the increased FiCD values of bilateral middle frontal gyrus and serum creatinine, urea, parathyroid hormone(PTH) levels and dialysis duration. Only the white matter hyperintensity(WMH) scores were significantly negatively correlated with the global FiCD value in multifactor regression analysis. Our results suggested that ESRD patients exhibited extensive impaired cortical structural connectivity, which was related to the severity of WMHs. A compensation mechanism of cortical structural recombination may play a role in how the brain adapts to maintain optimal network function. Additionally, the serum creatinine, urea and PTH levels may be risk factors for brain structural network decompensation in ESRD patients.