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Purpose: To develop and assess a three-dimensional (3D) self-gated technique for the evaluation of myocardial infarction (MI) in mouse model without the use of external electrocardiogram (ECG) trigger and respiratory motion sensor on a 3T clinical MR system. Methods: A 3D T1-weighted GRE sequence with stack-of-stars sampling trajectories was developed and performed on six mice with MIs that were injected with a gadolinium-based contrast agent at a 3T clinical MR system. Respiratory and cardiac self-gating signals were derived from the Cartesian mapping of the k-space center along the partition encoding direction by bandpass filtering in image domain. The data were then realigned according to the predetermined self-gating signals for the following image reconstruction. In order to accelerate the data acquisition, image reconstruction was based on compressed sensing (CS) theory by exploiting temporal sparsity of the reconstructed images. In addition, images were also reconstructed from the same realigned data by conventional regridding method for demonstrating the advantageous of the proposed reconstruction method. Furthermore, the accuracy of detecting MI by the proposed method was assessed using histological analysis as the standard reference. Linear regression and Bland-Altman analysis were used to assess the agreement between the proposed method and the histological analysis. Results: Compared to the conventional regridding method, the proposed CS method reconstructed images with much less streaking artifact, as well as a better contrast-to-noise ratio (CNR) between the blood and myocardium (4.1 ± 2.1 vs. 2.9 ± 1.1, p = 0.031). Linear regression and Bland-Altman analysis demonstrated that excellent correlation was obtained between infarct sizes derived from the proposed method and histology analysis. Conclusion: A 3D T1-weighted self-gating technique for mouse cardiac imaging was developed, which has potential for accurately evaluating MIs in mice at 3T clinical MR system without the use of external ECG trigger and respiratory motion sensor. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) has been applied to accelerate magnetic resonance imaging (MRI) for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-based reconstruction scheme, which combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT) with fast iterative shrinkage/soft thresholding algorithm (FISTA) to efficiently reduce such visual artifacts. The CDDDT-DWT has the characteristics of shift invariance, high degree, and a good directional selectivity. In addition, FISTA has an excellent convergence rate, and the design of FISTA is simple. Compared with conventional CS-based reconstruction methods, the experimental results demonstrate that this novel approach achieves higher peak signal-to-noise ratio (PSNR), larger signal-to-noise ratio (SNR), better structural similarity index (SSIM), and lower relative error. [ABSTRACT FROM AUTHOR]

Magnetic Resonance Imaging (MRI) is a leading diagnostic imaging modality that supports high contrast of soft tissues with no invasiveness or radiation. Nonetheless, it suffers from long scan time owing to the inherent physics in its data acquisition process, hampering its development and applications. Traditional strategies such as Compressed Sensing (CS) and Parallel Imaging (PI) allow for MRI acceleration via sub-sampling strategy, and multiple coils, respectively. When Deep Learning (DL) joins in, both strategies get re-vitalized to achieve even faster reconstruction in various reconstruction methods, among which the variational network is a previously proposed method that combines the mathematical structure of variational models with DL for fast MRI reconstruction. However, in our study we observe that the information of MR features is either not efficiently or explicitly exploited in former works based on the variational network. Instead, we introduce a variational network with explicit feature fusion that combines the CS, PI, with DL for accelerated multi-coil MRI reconstruction. By explicitly leveraging the extra information via feature fusion following feature extraction, our proposed method achieves comparably satisfying performance to the state-of-the-art methods without too much computation overhead on a public multi-coil brain dataset under 5-fold and 10-fold acceleration. [ABSTRACT FROM AUTHOR]

Abstract: Bone marrow mesenchymal stem cells (BMSCs) hold a great promising approach for the treatment of epilepsy owing to their distinctive characteristics and multi-potency. However, there is little research focusing on the multi-potency of BMSCs in the treatment of epilepsy, the present study was designed to examine the influence of genetically engineered BMSCs (GE-BMSCs) on the functional outcome in a rat model of epilepsy. First, Hes1 gene of BMSCs was genetically engineered by RNA interference (RNAi), and then the GABAergic differentiation of GE-BMSCs was tested in vitro. Second, the lithium chloride–pilocarpine induced epileptic rats were administrated with the GE-BMSCs, the behavioral observation and electroencephalography (EEG) monitoring was employed to analyze the functional outcome on the epileptic model at different time points (day 7, day 14, day 21 and day 28), followed by histological verification. In vitro test showed that Hes1 silencing could promote BMSCs to differentiate into GABAergic neuron-like cells. In vivo test showed that GE-BMSCs graft could further improve the functional recovery of the epileptic rats, and the GABAergic differentiation of grafted GE-BMSCs was correlated with the functional recovery. Taken together, these data suggest that GE-BMSCs can improve the functional outcome in a rat model of epilepsy. [Copyright &y& Elsevier]

A characteristic feature of atherosclerotic cardiovascular disease is the diffuse involvement of arteries across the entire human body and the presence of multiple, simultaneous lesions. The diffuse nature of this disease creates a unique challenge for early diagnosis and effective treatment. We believe that recent progress in the field of molecular MRI has opened new avenues towards solving the problem. A new technology has been developed that uses molecular MRI to monitor the migration and homing of hematopoietic stem-progenitor cells to injured arteries and atherosclerosis. In this Review, we introduce several novel technical developments in the field of molecular MRI of atherosclerosis, including advanced techniques for magnetic labeling of stem-progenitor cells and molecular MRI of hematopoietic bone marrow cells migrating to injured arteries and homing to atherosclerotic plaques. In addition, we examine molecular MRI of vascular gene therapy mediated by stem-progenitor cells. These new techniques provide the basis for the further development of in vivo MRI techniques to monitor stem-cell-mediated vascular gene therapy for multiple and diffuse atherosclerotic cardiovascular lesions. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging produces detailed anatomical and physiological images of the human body that can be used in the clinical diagnosis and treatment of diseases. However, MRI suffers its comparatively longer acquisition time than other imaging methods and is thus vulnerable to motion artifacts, which ultimately lead to likely failed or even wrong diagnosis. In order to perform faster reconstruction, deep learning-based methods along with traditional strategies such as parallel imaging and compressed sensing come into play in recent years in this field. Meanwhile, in order to better analyze the diseases, it is also often necessary to acquire images in the same region of interest under different modalities, which yield images with different contrast levels. However, most of these aforementioned methods tend to use single-modal images for reconstruction, neglecting the correlation and redundancy information embedded in MR images acquired with different modalities. While there are works on multi-modal reconstruction, the information is yet to be efficiently explored. In this paper, we propose an end-to-end neural network called MLMFNet, which helps the reconstruction of the target modality by using information from the auxiliary modality across feature channels and layers. Specifically, this is highlighted by three components: (I) An encoder based on UNet with a single-stream strategy that fuses auxiliary and target modalities; (II) a decoder that tends to multi-level features from all layers of the encoder, and (III) a channel attention module. Quantitative and qualitative analyses are performed on a public brain dataset and knee brain dataset, which show that the proposed method achieves satisfying results in MRI reconstruction within the multi-modal context, and also demonstrate its effectiveness and potential to be used in clinical practice. [ABSTRACT FROM AUTHOR]

In view of ever-increasing environmental pollution, there is an immediate requirement to promote cheap, multiplexed, sensitive and fast biosensing systems to monitor these pollutants or contaminants. Aptamers have shown numerous advantages in being used as molecular recognition elements in various biosensing devices. Graphene and graphene-based materials/nanohybrids combined with several detection methods exhibit great potential owing to their exceptional optical, electronic and physicochemical properties which can be employed extensively to monitor environmental contaminants. For environmental monitoring applications, aptamers have been successfully combined with graphene-based nanohybrids to produce a wide range of innovative methodologies. Aptamers are immobilized at the surface of graphene based nanohybrids via covalent and non-covalent strategies. This review highlights the design, working principle, recent developmental advances and applications of graphene based nanohybrid aptasensors (GNH-Apts) (since January 2014 to September 2021) with a special emphasis on two major signal-transduction methods, i.e., optical and electrochemical for the monitoring of pesticides, heavy metals, bacteria, antibiotics, and organic compounds from different environmental samples (e.g., water, soil and related). Lastly, the challenges confronted by scientists and the possible future outlook have also been addressed. It is expected that high-performance graphene-based nanohybrid aptasensors would find broad applications in the field of environmental monitoring. [ABSTRACT FROM AUTHOR]

Purpose: Ultrashort echo time (UTE) sequences play a key role in imaging and quantifying short T2 species. However, almost all of the relevant studies was conducted at relatively high fields. The purpose of this work was to further explore the feasibility of UTE imaging and T2* measurement for short T2 species at low fields.Methods: A 2D UTE sequence with an echo time (TE) of 0.37 ms was developed on a 0.35 T permanent magnet scanner. This sequence acquires multiecho images to fit the monoexponential signal decay model for quantitative T2* calculations. In the phantom experiments, MnCl2 solutions with different T2* values were used to assess the curve fitting model in low fields. In the in vivo experiments, T2* measurements were performed on the Achilles tendon of five normal volunteers.Results: The phantom studies showed a significant linear relationship between the MnCl2 solution concentration and R2* (1/T2*) values, which indicated the stability and accuracy of the T2* quantification model. The in vivo studies demonstrated that mean T2* value of Achilles tendon is 1.83 ± 0.21 ms, and the mean coefficient of determination (R-squared) was 0.996.Conclusions: Both phantom and in vivo experiments showed that UTE imaging and quantification for short T2 components were feasible at low field 0.35 T scanner. This pilot study presents preliminary conclusions for future work. [ABSTRACT FROM AUTHOR]

• The goal was to assess the effects of channel numbers on B 1 + mapping accuracy using the Bloch-Siegert shift method. • Identified a threshold where the increase in B 1 + map accuracy flattens out when the number of channels is greater than 12. • Provides a reference for coil selection, aiming to strike a balance between manufacturing cost and channel numbers. • Demonstrates the unique potential of the B 1 + mapping technique in evaluating brain imaging. This paper aims to investigate the impact of the channel numbers on the performance of B 1 + mapping, by using the Bloch-Siegert shift (BSS) method. B 1 + mapping plays a crucial role in various brain imaging protocols. We simulated the radiofrequency field of the human head model in six groups of multi-channel receive coil with a range of different channel numbers. MR signals were synthesized according to the standard BSS sequence, with quantified Gaussian added. Next, we combined the signals of each channel to reconstruct the B 1 + map by weighted averaging and maximum likelihood estimation strategies and evaluate the bias by relative standard deviation of each coil. The simulation results revealed that the accuracy of B 1 + maps improved with the increasing of channel numbers, meanwhile the per channel efficiency of B 1 + maps accuracy gradually decrease. Both trends slowed down when the channel numbers reached 12 or above. Our finding suggests that increasing the channel numbers can improve the accuracy of B 1 + map. However, a diminishing efficiency of per channel accuracy improvement was overserved, indicating that the relationship between quality of B 1 + map and the channel numbers is nonlinear. Based on these findings, our study provides a reference for determining channel numbers to achieve a balance of coil selection and manufacturing cost. It also provides a theoretical basis for evaluating other B 1 + mapping techniques. [Display omitted] [ABSTRACT FROM AUTHOR]

Purpose: Chest X‐ray is one of the most common examinations for diagnosing heart and lung diseases. Due to the existing of a large number of clinical cases, many automated diagnosis algorithms based on chest X‐ray images have been proposed. To our knowledge, almost none of the previous auto‐diagnosis algorithms consider the effect of relative location information on disease incidence. In this study, we propose to use relative location information to assist the identification of thorax diseases. Method: In this work, U‐Net is used to segment lung and heart from chest image. The relative location maps are computed through Euclidean distance transformation from segmented masks. By introducing the relative location information into the network, the usual location of disease is combined with the incidence. The proposed network is the fusion of two branches: mask branch and image branch. A mask branch is designed to be a bottom‐up and top‐down structure to extract relative location information. The structure has a large receptive field, which can extract more information for large lesion and contextual information for small lesion. The features learned from mask branch are fused with image branch, which is a 121‐layers DenseNet. Results: We compare our proposed method with four state‐of‐the‐art methods on the largest public chest X‐ray dataset: ChestX‐ray14. The proposed method achieves the area under a curve of 0.820, which outperforms all the existing models and algorithms. Conclusion: This paper proposed a dense network with relative location information to identify thorax disease. The method combines the usual location of disease with the incidence for the first time and performs good. [ABSTRACT FROM AUTHOR]

Chemical exchange saturation transfer (CEST) MRI provides a sensitive detection mechanism for imaging dilute labile protons, complementing the routine radiological exams. Enormous progress has been achieved in CEST MRI and image analysis, from the mathematical modeling, CEST agent design, and most importantly, increasing adoption of CEST imaging in the clinical setting. Therefore, CEST imaging represents an emerging field that involves multiple disciplines and together made a remarkable transition from the simplistic CEST-weighted MRI to quantitative CEST (qCEST) analysis. This review focuses on the recent advancements in CEST quantification techniques and findings of in vivo CEST imaging in representative disorders of ischemia, tumor, and epilepsy. In addition, limitations of current CEST methodologies are examined that should help guide future development of more sensitive and quantitative CEST imaging techniques and ultimately, facilitate their adoption in the clinical setting. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) is central to the communication with machines and among ourselves, and NLP research field has long sought to produce human‐quality language. Identification of informative criteria for measuring NLP‐produced language quality will support development of ever‐better NLP tools. The authors hypothesize that mentalizing network neural activity may be used to distinguish NLP‐produced language from human‐produced language, even for cases where human judges cannot subjectively distinguish the language source. Using the social chatbots Google Meena in English and Microsoft XiaoIce in Chinese to generate NLP‐produced language, behavioral tests which reveal that variance of personality perceived from chatbot chats is larger than for human chats are conducted, suggesting that chatbot language usage patterns are not stable. Using an identity rating task with functional magnetic resonance imaging, neuroimaging analyses which reveal distinct patterns of brain activity in the mentalizing network including the DMPFC and rTPJ in response to chatbot versus human chats that cannot be distinguished subjectively are conducted. This study illustrates a promising empirical basis for measuring the quality of NLP‐produced language: adding a judge's implicit perception as an additional criterion. [ABSTRACT FROM AUTHOR]

Super-porous hydrogels are considered a potential drug delivery network for the sedation of gastric mechanisms with retention windows in the abdomen and upper part of the gastrointestinal tract (GIT). In this study, a novel pH-responsive super-porous hybrid hydrogels (SPHHs) was synthesized from pectin, poly 2-hydroxyethyl methacrylate (2HEMA), and N, N methylene-bis-acrylamide (BIS) via the gas-blowing technique, and then loaded with a selected drug (amoxicillin trihydrate, AT) at pH 5 via an aqueous loading method. The drug-loaded SPHHs-AT carrier demonstrated outstanding (in vitro) gastroretentive drug delivery capability. The study attributed excellent swelling and delayed drug release to acidic conditions at pH 1.2. Moreover, in vitro controlled-release drug delivery systems at different pH values, namely, 1.2 (97.99%) and 7.4 (88%), were studied. These exceptional features of SPHHs—improved elasticity, pH responsivity, and high swelling performance—should be investigated for broader drug delivery applications in the future. [ABSTRACT FROM AUTHOR]

The high computational complexity of tree-based multipath search approaches makes putting them into practical use difficult. However, reselection of candidate atoms could make the search path more accurate and efficient. We propose a multipath greedy approach called fast sparsity adaptive multipath matching pursuit (fast SAMMP), which performs a sparsity adaptive tree search to find the sparsest solution with better performances. Each tree branch acquires K atoms, and fast SAMMP reselects the best K atoms among 2K atoms. Fast SAMMP adopts sparsity adaptive techniques that allow more practical applications for the algorithm. We demonstrated the reconstruction performances of the proposed fast scheme on both synthetically generated one-dimensional signals and two-dimensional images using Gaussian observation matrices. The experimental results indicate that fast SAMMP achieves less reconstruction time and a much higher exact recovery ratio compared with conventional algorithms. [ABSTRACT FROM AUTHOR]

Introduction: Empathy traits are highly heritable and linked with reward processing. It is implicated that common variations of the oxytocin-receptor gene (OXTR) play a modulatory effect on empathic performance. However, it is unclear about the neural substrates underlying the modulatory effect of the OXTR genotype on empathic performance. This study aimed to characterize the modulatory effect of common OXTR variations on reward-circuitry function and its relationship with empathy. Methods: Based on the seed of the nucleus accumbens (NAcc; a key hub of reward circuitry), we examined differences in spontaneous local activity and functional connectivity between OXTR rs2268493 genotype groups and their relationship with empathic performance among 402 high-homogeneity participants. Results: Comparing with C carriers (CC/CT) group, the individuals with the rs2268493 TT genotype exhibited lower functional connectivity of the right NAcc with the medial prefrontal cortex (mPFC) and inferior frontal gyrus. Similarly lower functional connectivity was found between the left NAcc and mPFC. Consequently, no significant difference was found in the spontaneous local activity of NAcc. Discussion: Our findings suggested that common OXTR variations have a modulatory effect on the connection of the NAcc with the hub of empathic networks (mPFC and IFG), which may provide insight on the neural substrate underlying the modulatory effect of OXTR on empathic behavior. [ABSTRACT FROM AUTHOR]

Alterations in brain reactions to alcohol-related cues are a neurobiological characteristic of alcohol dependence (AD) and a prospective target for achieving substantial treatment effects. However, a robust prediction of the differences in inpatients' brain responses to alcohol cues during the treatment process is still required. This study offers a data-driven approach for classifying AD inpatients undertaking alcohol treatment protocols based on their brain responses to alcohol imagery with and without drinking actions. The brain activity of thirty inpatients with AD undergoing treatment was scanned using functional magnetic resonance imaging (fMRI) while seeing alcohol and matched non-alcohol images. The mean values of brain regions of interest (ROI) for alcohol-related brain responses were obtained using general linear modeling (GLM) and subjected to hierarchical clustering analysis. The proposed classification technique identified two distinct subgroups of inpatients. For the two types of cues, subgroup one exhibited significant activation in a wide range of brain regions, while subgroup two showed mainly decreased activation. The proposed technique may aid in detecting the vulnerability of the classified inpatient subgroups, which can suggest allocating the inpatients in the classified subgroups to more effective therapies and developing prognostic future relapse markers in AD. [ABSTRACT FROM AUTHOR]

Background and purpose: Cerebral amyloid angiopathy (CAA) is characterized by β‐amyloid deposition in cortical and leptomeningeal arterioles, which might result from glymphatic dysfunction. The aim was to explore glymphatic function in CAA using the non‐invasive diffusion tensor image analysis along the perivascular space method. Methods: Sixty‐three patients with CAA were prospectively recruited together with seventy age‐ and sex‐matched normal controls. The Mini‐Mental State Examination and Montreal Cognitive Assessment were applied to screen global cognitive status. Magnetic resonance imaging scans were conducted to calculate the index for diffusivity along the perivascular space (ALPS index), and linear regression models were used to assess its relationships with cerebral small vessel disease (CSVD) markers, cognitive status and blood biomarkers. Cox proportional hazard models were applied to explore the role of the baseline ALPS index in disease recurrence. Results: Patients with CAA exhibited a lower ALPS index than controls globally (p < 0.001). In addition, a lower ALPS index was related to more enlarged perivascular space in basal ganglia (p = 0.026), more lacunes (p < 0.001), higher white matter hyperintensity Fazekas score (p = 0.049), elevated total magnetic resonance imaging burden of CSVD (p = 0.034) and lower Mini‐Mental State Examination (p = 0.001) as well as Montreal Cognitive Assessment (p < 0.001) in CAA. During a median follow‐up of 4.1 years, a higher ALPS index was associated with lower disease recurrence (p = 0.022). The ALPS index was also negatively correlated with serum soluble intercellular adhesion molecule‐1, neurofilament light and chitinase‐3‐like protein 1 in CAA. Conclusions: Patients with CAA showed impaired glymphatic function. The ALPS index was significantly related to CSVD severity, cognitive impairment and disease recurrence in CAA. [ABSTRACT FROM AUTHOR]

Temporal interference transcranial alternating current stimulation (TI-tACS) is a new technique of noninvasive brain stimulation. Previous studies have shown the effectiveness of TI-tACS in stimulating brain areas in a selective manner. However, its safety in modulating human brain neurons is still untested. In this study, 38 healthy adults were recruited to undergo a series of neurological and neuropsychological measurements regarding safety concerns before and after active (2 mA, 20/70 Hz, 30 min) or sham (0 mA, 0 Hz, 30 min) TI-tACS. The neurological and neuropsychological measurements included electroencephalography (EEG), serum neuron-specific enolase (NSE), the Montreal Cognitive Assessment (MoCA), the Purdue Pegboard Test (PPT), an abbreviated version of the California Computerized Assessment Package (A-CalCAP), a revised version of the Visual Analog Mood Scale (VAMS-R), a self-assessment scale (SAS), and a questionnaire about adverse effects (AEs). We found no significant difference between the measurements of the active and sham TI-tACS groups. Meanwhile, no serious or intolerable adverse effects were reported or observed in the active stimulation group of 19 participants. These results support that TI-tACS is safe and tolerable in terms of neurological and neuropsychological functions and adverse effects for use in human brain stimulation studies under typical transcranial electric stimulation (TES) conditions (2 mA, 20/70 Hz, 30 min). [ABSTRACT FROM AUTHOR]

Microwave ablation (MWA) is a form of thermal therapy that uses the heating effects of electromagnetic waves in the microwave energy spectrum to kill undesired or diseased tissues. Slot coaxial antennas are the most popular antennas used in MWA due to their minimal invasiveness and low cost. MWA with slot coaxial antenna has been proven to be very promising for the treatment of hepatic tumors, but the residual ‘tail’ seen in many of the specific absorption rate (SAR) patterns inevitably damages surrounding healthy tissues. This is because a treatment that fully conforms to the shape of liver tumor is difficult to achieve with conventional MWA antennas even with current optimization strategies. For the first time, we report a novel design for a coaxial antenna with greatly increased possibility of conformal treatment, a coaxial multi-slot antenna with multiple, preoperative, adjustable, slots for conformal microwave ablation. Theoretical simulations revealed that our coaxial multi-slot antenna could greatly decrease over-treatment compared with the conventional coaxial-slot antenna. Additionally, the coaxial-slot antenna with ten slots generates the smallest over-treatment region for spherical tumors, the most common hepatic tumor shape. For tumors with non-spherical shapes, optimal treatment could be routinely accomplished by selecting coaxial-slot antennas with different numbers of slots. [ABSTRACT FROM AUTHOR]

Objective: Electroconvulsive therapy (ECT), a rapidly acting treatment for major depressive disorder (MDD), has been reported to regulate brain networks. Nodes and their connections are the main components of the brain network and are essential for establishing and maintaining effective information transmission. This study aimed to evaluate the role of nodes in mediating antidepressant effects of ECT.Methods: Voxel-based nodal degree analysis was performed in 42 patients with MDD receiving ECT and 42 matched healthy controls at two time points to identify the nodal changes induced by ECT. Verification analysis was evaluated in a second, independent cohort of 23 MDD patients.Results: MDD patients showed improved nodal degree of the bilateral angular cortex (AG), precuneus, inferior frontal gyrus (IFG) and the right superior frontal gyrus (SFG) after ECT, and the increased nodal degree index (IND) rate of the AG and precuneus were negatively correlated to the depressive changes following ECT. Furthermore, validation analysis revealed a similar pattern of IND abnormalities in the first and second cohort of MDD patients.Conclusion: ECT regulates the disrupted nodal degree of the AG and precuneus to achieve an antidepressant effect. This study may provide further insights into the pathogenesis of depression and provide potential targets for antidepressant pharmacotherapies. [ABSTRACT FROM AUTHOR]

Compressed sensing magnetic resonance imaging (CS-MRI) greatly accelerates the acquisition process and yield considerable reconstructed images. Deep learning was introduced into CS-MRI to further speed up the reconstruction process and improve the image quality. Recently, generative adversarial network (GAN) using two-stage cascaded U-Net structure as generator has been proven to be effective in MRI reconstruction. However, previous cascaded structure was limited to few feature information propagation channels thus may lead to information missing. In this paper, we proposed a GAN-based model, DBGAN, for MRI reconstruction from undersampled k-space data. The model uses cross-stage skip connection (CSSC) between two end-to-end cascaded U-Net in our generator to widen the channels of feature propagation. To avoid discrepancy between training and inference, we replaced classical batch normalization (BN) with instance normalization (IN). A stage loss is involved in the loss function to boost the training performance. In addition, a bilinear interpolation decoder branch is introduced in the generator to supplement the missing information of the deconvolution decoder. Tested under five variant patterns with four undersampling rates on different modality of MRI data, the quantitative results show that DBGAN model achieves mean improvements of 3.65 dB in peak signal-to-noise ratio (PSNR) and 0.016 in normalized mean square error (NMSE) compared with state-of-the-art GAN-based methods on T1-Weighted brain dataset from MICCAI 2013 grand challenge. The qualitative visual results show that our method can reconstruct considerable images on brain and knee MRI data from different modality. Furthermore, DBGAN is light and fast – the model parameters are fewer than half of state-of-the-art GAN-based methods and each 256 × 256 image is reconstructed in 60 milliseconds, which is suitable for real-time processing. [ABSTRACT FROM AUTHOR]

Decoding brain cognitive states from neuroimaging signals is an important topic in neuroscience. In recent years, deep neural networks (DNNs) have been recruited for multiple brain state decoding and achieved good performance. However, the open question of how to interpret the DNN black box remains unanswered. Capitalizing on advances in machine learning, we integrated attention modules into brain decoders to facilitate an in‐depth interpretation of DNN channels. A four‐dimensional (4D) convolution operation was also included to extract temporo‐spatial interaction within the fMRI signal. The experiments showed that the proposed model obtains a very high accuracy (97.4%) and outperforms previous researches on the seven different task benchmarks from the Human Connectome Project (HCP) dataset. The visualization analysis further illustrated the hierarchical emergence of task‐specific masks with depth. Finally, the model was retrained to regress individual traits within the HCP and to classify viewing images from the BOLD5000 dataset, respectively. Transfer learning also achieves good performance. Further visualization analysis shows that, after transfer learning, low‐level attention masks remained similar to the source domain, whereas high‐level attention masks changed adaptively. In conclusion, the proposed 4D model with attention module performed well and facilitated interpretation of DNNs, which is helpful for subsequent research. [ABSTRACT FROM AUTHOR]

Visual-spatial working memory (vsWM) impairment in treatment-resistant schizophrenia (TRS) currently has no satisfactory treatment. Our study aimed to improve vsWM function in TRS through intermittent theta burst stimulation (iTBS) using neuronavigation equipment to target the left dorsolateral prefrontal cortex. TRS patients (n = 59) were randomly allocated to receive iTBS (n = 33) or a sham treatment (n = 26) over 2 weeks. The participants including TRS patients and healthy controls (HCs) performed the vsWM n-back task, and TRS patients' neuroimaging data were acquired before and after treatment. All patients also underwent a battery of symptom measures to assess the severity of illness. The main outcome measure was the accuracy (ACC) of n-back target responses, particularly 3-back ACC. The iTBS group showed considerable improvement in n-back ACC compared to the sham group, especially 3-back ACC. After iTBS, performance on the n-back task was comparable to that of HCs. The interaction (group × time) results showed increased fractional amplitude of low frequency fluctuations (fALFF) in the right occipital areas and decreased fALFF in the right precuneus. However, there was a negative correlation between the 3-back ACC and improved clinical symptoms scores. Improvements in 3-back ACC were positively correlated with activity in the right visual cortex. Our study suggested that 2 weeks of iTBS intervention may be a novel, efficacious treatment for vsWM deficits in TRS, which can modulate the activity of local brain regions. iTBS can provide a solution for clinical treatment of TRS and may help patients approach normalcy. • Visual-spatial working memory (vsWM) impairment as ubiquitous impairment in treatment-resistant schizophrenia (TRS) during the prodromal stage and persists throughout the disease. • rTMS might be considered a safe technique for ameliorating working memory deficits. • iTBS group showed large improvements in the n-back ACC, especially 3 back, which modified local regions within the visual cortex and right precuneus. • There was a significant correlation between the increased ACC of 3-back and the improved clinical symptoms and increased activity in visual cortex. • iTBS might be a potential cognitive enhancing tool, particularly for vsWM. [ABSTRACT FROM AUTHOR]

Hybrid functional biomaterials are attracting increasing interest due to their biocompatibility and therapeutic and diagnostic characteristics. The theranostic properties of functional biomaterials favor their application. When these materials are responded to stimuli, they confer target site delivery. Although various types of nanocomposites have been developed for drug delivery and diagnostics, no ideal composites have been reported yet. Here, we report the synthesis of graphene oxide–zinc ferrite hybrid nanocomposites (GO-ZnFe 2 O 4) conjugated with doxorubicin (GO-ZnFe 2 O 4 /DOX) for cancer therapy and magnetic resonance (MR) imaging-based diagnosis. The optical properties, crystal phase, particle size, functional groups, elemental composition, surface morphology, and magnetism of GO-ZnFe 2 O 4 nanocomposites were characterized using state-of-the-art available techniques, including Fourier-Transform Infrared Spectroscopy (FTIR), Ultraviolet visible spectroscopy (UV–Vis), Transmission electron microscopy (TEM), X-ray powder diffraction (XRD), Dynamic light scattering (DLS), Vibrating sample magneto meter (VSM) Scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and X-ray photoelectron spectra (XPS). The in vitro analysis showed that GO-ZnFe 2 O 4 conjugated with DOX is more cytotoxic than GO-ZnFe 2 O 4. GO-ZnFe 2 O 4 /DOX induced the production of reactive oxygen species (ROS), which induced damage to nuclear DNA and mitochondrial DNA (mtDNA) when internalized by cells. This damage consequently drove mitochondrial malfunction and ultimately the apoptosis of cancer cells. Further studies were performed to investigate the diagnostic efficacy of these nanocomposites using MR imaging. GO-ZnFe 2 O 4 /DOX nanocomposites were developed and successfully employed in the MR imaging of HeLa cells. As shown in the present study, GO-ZnFe 2 O 4 /DOX might play a potential role in the development of chemotherapy and noninvasive MR imaging of tumor cells. [ABSTRACT FROM AUTHOR]

Microwave ablation has been considered as a promising therapy for the treatment of liver tumors. However, many antennas proposed in previous literature generated ellipsoidal ablation and also were not suitable for percutaneous intervention due to the lack of mechanical properties. To address these problems, a closed dual‐slot antenna was investigated to generate spherical ablation and designed with zirconia tip, which facilitated its ability to puncture into tissues, and metal tube, which provided mechanical strength for percutaneous puncture. A numerical model was constructed to optimize antenna geometry and analyze antenna performance. The reflection coefficient, specific absorption rate, and temperature distribution of the closed and conventional dual‐slot antennas were all simulated by using finite element method. The two antennas were also validated with ex vivo porcine livers, finding a great agreement between simulated and experimental results. The closed dual‐slot antenna generated a more spherical ablation zone (34.0 mm × 37.2 mm vs. 29.1 mm × 48.5 mm at 50 W for 10 min) with a lower reflection coefficient (−30.3 vs. −9.5 dB) than did conventional dual‐slot antenna, and showed qualified mechanical properties for clinical application, which may be further applied to clinical percutaneous intervention to treat liver tumors effectively. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid optimization algorithm that combines linear programming (LP), genetic algorithm (GA), and nonlinear programming (NLP) to achieve the optimal design of highly homogeneous superconducting magnets for MRI systems. Initially, the predetermined rectangular region is divided into an array of superconductor coils. Then, linear programming is utilized to minimize the consumption of superconducting conductors as the objective function and to obtain the nonzero current regions by considering the field peak-to-peak uniformity in the diameter of the spherical volume (DSV) and the range of the 5 Gauss stray field as constraints. Subsequently, the genetic algorithm is employed to convert the nonzero current regions into coils with rectangular cross-sections. Finally, the NLP is applied to adjust the position of each coil to obtain the magnet criteria. An illustrative example is provided: an actively shielded MRI superconducting magnet with a center field strength of 1.5 T. The effectiveness of this optimization method is demonstrated through the design, electromagnetic analysis, and stress analysis conducted on this example. • Summarize the advantages and disadvantages of various representative methods. • A novel hybrid optimization method for the design of MRI superconducting magnets. • A medical-grade 1.5T superconducting magnet for MRI was designed. • A analysis of the electromagnetic field, stress, and other related properties. [ABSTRACT FROM AUTHOR]

Background: The white matter hyperintensities (WMHs) are considered as one of the core neuroimaging findings of cerebral small vessel disease and independently associated with cognitive deficit. The parietal lobe is a heterogeneous area containing many subregions and play an important role in the processes of neurocognition.Objective: To explore the relationship between parietal subregions alterations and cognitive impairments in WHMs.Methods: Resting-state functional connectivity (rs-FC) analyses of parietal subregions were performed in 104 right-handed WMHs patients divided into mild (n = 39), moderate (n = 37), and severe WMHs (n = 28) groups according to the Fazekas scale and 36 healthy controls. Parietal subregions were defined using tractographic Human Brainnetome Atlas and included five subregions for superior parietal lobe, six subregions for inferior parietal lobe (IPL), and three subregions for precuneus. All participants underwent a neuropsychological test battery to evaluate emotional and general cognitive functions.Results: Differences existed between the rs-FC strength of IPL_R_6_2 with the left anterior cingulate gyrus, IPL_R_6_3 with the right dorsolateral superior frontal gyrus, and the IPL_R_6_5 with the left anterior cingulate gyrus. The connectivity strength between IPL_R_6_3 and the left anterior cingulate gyrus were correlated with AVLT-immediate and AVLT-recognition test in WMHs.Conclusion: We explored the roles of parietal subregions in WMHs using rs-FC. The functional connectivity of parietal subregions with the cortex regions showed significant differences between the patients with WMHs and healthy controls which may be associated with cognitive deficits in WMHs. [ABSTRACT FROM AUTHOR]

With the growing population and rapid change in the social environment, nurses in China are suffering from high rates of stress; however, the neural mechanism underlying this occupation related stress is largely unknown. In this study, mental status was determined for 81 nurses and 61 controls using the Symptom Checklist 90 (SCL‐90) scale. A subgroup (n = 57) was further scanned by resting‐state functional MRI with two sessions. Based on the SCL‐90 scale, "somatic complaints" and "diet/sleeping" exhibited the most prominent difference between nurses and controls. This mental health change in nurses was further supported by the spatial independent component analysis on functional MRI data. First, dynamic functional connectome analysis identified two discrete connectivity configurations (States I and II). Controls had more time in the State I than II, while the nurses had more time in the State II than I. Second, nurses showed a similar static network topology as controls, but altered dynamic properties. Third, the symptom‐imaging correlation analysis suggested the functional alterations in nurses as potential imaging biomarkers indicating a high risk for "diet/sleeping" problems. In summary, this study emphasized the high risk of mental deficits in nurses and explored the underlying neural mechanism using dynamic brain connectome, which provided valuable information for future psychological intervention. [ABSTRACT FROM AUTHOR]

A large proportion of patients with obsessive–compulsive disorder (OCD) respond unsatisfactorily to pharmacological and psychological treatments. An alternative novel treatment for these patients is repetitive transcranial magnetic stimulation (rTMS). This study aimed to investigate the underlying neural mechanism of rTMS treatment in OCD patients. A total of 37 patients with OCD were randomized to receive real or sham 1‐Hz rTMS (14 days, 30 min/day) over the right pre‐supplementary motor area (preSMA). Resting‐state functional magnetic resonance imaging data were collected before and after rTMS treatment. The individualized target was defined by a personalized functional connectivity map of the subthalamic nucleus. After treatment, patients in the real group showed a better improvement in the Yale–Brown Obsessive Compulsive Scale than the sham group (F1,35 = 6.0, p =.019). To show the neural mechanism involved, we identified an "ideal target connectivity" before treatment. Leave‐one‐out cross‐validation indicated that this connectivity pattern can significantly predict patients' symptom improvements (r =.60, p =.009). After real treatment, the average connectivity strength of the target network significantly decreased in the real but not in the sham group. This network‐level change was cross‐validated in three independent datasets. Altogether, these findings suggest that personalized magnetic stimulation on preSMA may alleviate obsessive–compulsive symptoms by decreasing the connectivity strength of the target network. [ABSTRACT FROM AUTHOR]

• Abnormalities of WM functional networks are beyond temporal regions in mTLE. • The deep WM networks as a key node may associate with massive dysfunction in mTLE. • Functional connectivity of WM could be potentially used in the diagnosis of mTLE. Mesial temporal lobe epilepsy (mTLE) has been considered a network disorder disease in which brain regions extending beyond the epileptogenic zones are always affected. However, abnormalities in white matter (WM) functional networks and their associations with widespread network dysfunction are still being identified in mTLE. Accordingly, we investigated the altered functional activities in WM networks in mTLE using fMRI, which has recently been used to probe WM function. We collected resting-state fMRI data from 39 unilateral mTLE patients with hippocampal sclerosis and 29 healthy controls. Eleven WM networks were clustered according to temporal correlation profile. The functional connectivity (FC) of the WM networks were evaluated and compared between the two groups. Furthermore, we assessed the capacity of WM FC for seizure lateralization. According to our analysis, mTLE led to decreased FC within deep WM networks. In addition, the cortical regions involved in seizure propagation and several brain regions displaying interhemispheric disruption showed enhanced functional coupling with deep WM networks. FCs between the ipsilateral deep WM networks and the insula, temporal lobe, and supramarginal gyrus demonstrated positive correlation with seizure frequency. Moreover, the seizure onset zones of 33 patients out of 39 patients could be correctly lateralized. Our findings reveal functional disruptions in WM networks extending to extratemporal regions, supporting the network disorder hypothesis and suggesting that deep WM networks are key network nodes associated with massive dysfunction in mTLE. Moreover, the FC of the WM represents a potentially useful functional imaging measure for the diagnosis of mTLE. [ABSTRACT FROM AUTHOR]

In the past two decades, significant progress has been made in the past two decades towards the understanding of the basic mechanisms underlying cancer growth and angiogenesis. In this context, receptor tyrosine kinases (RTKs) play a pivotal role in cell proliferation, differentiation, growth, motility, invasion, and angiogenesis, all of which contribute to tumor growth and progression. Mutations in RTKs lead to abnormal signal transductions in several pathways such as Ras-Raf, MEK-MAPK, PI3K-AKT and mTOR pathways, affecting a wide range of biological functions including cell proliferation, survival, migration and vascular permeability. Increasing evidence demonstrates that multiple kinases are involved in angiogenesis including RTKs such as vascular endothelial growth factor, platelet derived growth factor, epidermal growth factor, insulin-like growth factor-1, macrophage colony-stimulating factor, nerve growth factor, fibroblast growth factor, Hepatocyte Growth factor, Tie 1 & 2, Tek, Flt-3, Flt-4 and Eph receptors. Overactivation of RTKs and its downstream regulation is implicated in tumor initiation and angiogenesis, representing one of the hallmarks of cancer. This review discusses the role of RTKs, PI3K, and mTOR, their involvement, and their implication in pro-oncogenic cellular processes and angiogenesis with effective approaches and newly approved drugs to inhibit their unrestrained action. [ABSTRACT FROM AUTHOR]

White matter hyperintensities (WMH) are common in elderly individuals and cause brain network deficits. However, it is still unclear how the global brain network is affected by the focal WMH. We aimed to investigate the diffusion of WMH‐related deficits along the connecting white matters (WM). Brain magnetic resonance imaging data and neuropsychological evaluations of 174 participants (aged 74 ± 5 years) were collected and analyzed. For each participant, WMH lesions were segmented using a deep learning method, and 18 major WM tracts were reconstructed using automated quantitative tractography. The diffusion characteristics of distal WM tracts (with the WMH penumbra excluded) were calculated. Multivariable linear regression analysis was performed. We found that a high burden of tract‐specific WMH was related to worse diffusion characteristics of distal WM tracts in a wide range of WM tracts, including the forceps major (FMA), forceps minor (FMI), anterior thalamic radiation (ATR), cingulum cingulate gyrus (CCG), corticospinal tract (CST), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus‐parietal (SLFP), superior longitudinal fasciculus‐temporal (SLFT), and uncinate fasciculus (UNC). Furthermore, a higher mean diffusivity (MD) of distal tracts was linked to worse attention and executive function in the FMI, right CCG, left ILF, SLFP, SLFT, and UNC. The effect of WMH on the microstructural integrity of WM tracts may propagate along tracts to distal regions beyond the penumbra and might eventually affect attention and executive function. [ABSTRACT FROM AUTHOR]

[Display omitted] • Provide a new, low-cost, and highly accurate low-field MRI-guided diagnosis and treatment scheme for breast cancer patients. • A new open device for MRI-guided breast interventional diagnosis and treatment is developed. • A unilateral coil for breast interventional therapy with good openness is customized and its overall performance is better than that of the commercial breast imaging coil. • A simple and reliable lesion localization design combined for breast intervention device is proposed. With the incidence of breast cancer rising to the top among female malignant tumors, magnetic resonance images guided breast biopsy intervention and minimally invasive treatment have developed as a clinically practical research issue. High field studies have shown the diagnostic value of breast MRI, but the examination costs greatly exceed those of competing conventional mammography. In this case, low-field MRI cannot merely provide typical MRI contrast, but also significantly reduce the cost of diagnosis and treatment for breast cancer patients. This work describes a unilateral breast coil and prototype intervention device, which provides a customized solution for low-field MRI-guided breast intervention. Results demonstrate that the low-field MRI breast intervention device facilitates medical intervention procedures. And the designed positioning device can locate the target lesion within 2–3 mm accuracy. Phantom tests with the customized unilateral coil indicate that the open loops perform as well as the 4-channel commercial closed breast coil, presenting a relatively good SNR (signal-to-noise ratio) and uniformity characteristics. MR scanning images of the volunteer breast using the breast intervention coil also show high SNR, which lays a foundation for further implementation of image-guided breast interventional minimally invasive surgery with the low-field MRI system. [ABSTRACT FROM AUTHOR]

Metal artifacts commonly appear in computed tomography (CT) images of the patient body with metal implants and can affect disease diagnosis. Known deep learning and traditional metal trace restoring methods did not effectively restore details and sinogram consistency information in X-ray CT sinograms, hence often causing considerable secondary artifacts in CT images. In this paper, we propose a new cross-domain metal trace restoring network which promotes sinogram consistency while reducing metal artifacts and recovering tissue details in CT images. Our new approach includes a cross-domain procedure that ensures information exchange between the image domain and the sinogram domain in order to help them promote and complement each other. Under this cross-domain structure, we develop a hierarchical analytic network (HAN) to recover fine details of metal trace, and utilize the perceptual loss to guide HAN to concentrate on the absorption of sinogram consistency information of metal trace. To allow our entire cross-domain network to be trained end-to-end efficiently and reduce the graphic memory usage and time cost, we propose effective and differentiable forward projection (FP) and filtered back-projection (FBP) layers based on FP and FBP algorithms. We use both simulated and clinical datasets in three different clinical scenarios to evaluate our proposed network’s practicality and universality. Both quantitative and qualitative evaluation results show that our new network outperforms state-of-the-art metal artifact reduction methods. In addition, the elapsed time analysis shows that our proposed method meets the clinical time requirement. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) is widely used to get the information of anatomical structure and physiological function with the advantages of high resolution and non-invasive scanning. But the long acquisition time limits its application. To reduce the time consumption of MRI, compressed sensing (CS) theory has been proposed to reconstruct MRI images from undersampled k-space data. But conventional CS methods mostly use iterative methods that take lots of time. Recently, deep learning methods are proposed to achieve faster reconstruction, but most of them only pay attention to a single domain, such as the image domain or k-space. To take advantage of the feature representation in different domains, we propose a cross-domain method based on deep learning, which first uses convolutional neural networks (CNNs) in the image domain, k-space and wavelet domain simultaneously. The combined order of the three domains is also first studied in this work, which has a significant effect on reconstruction. The proposed IKWI-net achieves the best performance in various combinations, which utilizes CNNs in the image domain, k-space, wavelet domain and image domain sequentially. Compared with several deep learning methods, experiments show it also achieves mean improvements of 0.91 dB in peak signal-to-noise ratio (PSNR) and 0.005 in structural similarity (SSIM). [ABSTRACT FROM AUTHOR]

Background: Somatic anxiety patients complain of physical symptoms and exhibit repeated checking behavior. Overgeneralization of fear is a characteristic of anxiety disorders. However, the role of fear in the somatic anxiety patients remains unclear. We hypothesized that somatic anxiety patients have abnormal fear circuits, including the amygdala, hippocampus, thalamus, orbitofrontal cortex and anterior cingulate cortex, which can aggravate physical symptoms.Methods: 33 anxiety patients and 25 healthy controls (HCs) were recruited. The severity of the anxiety and somatic symptoms was assessed with the Hamilton anxiety scale and the 15-item somatic symptom severity scale from the Patient Health Questionnaire (PHQ-15). The amplitude of low-frequency fluctuations (ALFF) in resting-state functional magnetic resonance imaging was used to assess abnormalities in the fear circuit. We compared the ALFF between patients and HCs with respect to the fear circuit and conducted correlation analysis to investigate the relationship between somatic symptoms and the ALFF in abnormal cerebral regions.Results: The ALFF of the left thalamus and left hippocampus was significantly higher in the patient group than the HC group, and was positively correlated with the PHQ-15 values.Limitations: We did not divide the patient group into drug treated or drug free in our subgroup analysis. There was a lack of the paradigm to test the generalization of fear for patients in this study. Furthermore, the small sample size may have affected the results.Conclusion: Somatic symptoms in patients with anxiety are related to abnormal fear circuits, whose degree of abnormality is associated with symptom severity. [ABSTRACT FROM AUTHOR]

Purpose: Metal implants in the patient's body can generate severe metal artifacts in x‐ray computed tomography (CT) images. These artifacts may cover the tissues around the metal implants in CT images and even corrupt the tissue regions, thus affecting disease diagnosis using these images. Previous deep learning metal trace inpainting methods used both valid pixels of uncorrupted areas and invalid pixels of corrupted areas to patch metal trace (i.e., the holes of removed metal‐corrupted regions). Such methods cannot recover fine details well and often suffer information mismatch due to interference of invalid pixels, thus incurring considerable secondary artifacts. In this paper, we develop a new irregular metal trace inpainting network for reducing metal artifacts. Methods: We develop a new deep learning network to patch irregular metal trace in metal‐corrupted sinograms to reduce metal artifacts for isometric fan‐beam CT. Our new method patches irregular metal trace in CT sinograms using only valid pixels, avoiding interference from invalid pixels. Furthermore, to enable the inpainting network to recover as many details as possible, we design an auxiliary inpainting network to suppress the probable secondary artifacts in CT images to assist fine detail restoration. The image produced by the auxiliary network is then projected onto a sinogram via a forward projection (FP) algorithm and is fused with the sinogram predicted by the inpainting network in order to predict the final recovered sinogram. Our entire network is trained end‐to‐end to extract cross‐domain information between the sinogram domain and CT image domain. Results: We compare our proposed method with two traditional and four deep learning‐based metal trace inpainting methods, and with an iterative reconstruction method on four datasets: dental fillings (panoramic and local perspectives), hip prostheses, and spine fixations. We use both quantitative and qualitative indices to evaluate our method, and the analyses suggest that our method reduces the most metal artifacts and produces the best quality CT images. Additionally, our proposed method takes 0.1512 s on average to process a CT slice, which meets the clinical requirement. Conclusions: This paper proposes a new deep learning network to patch irregular metal trace in corrupted sinograms to reduce metal artifacts. Our method restores more fine details in irregular metal trace and has a superior capability on metal artifact reduction compared with state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

The hydraulic permeability of the lipid bilayer membrane of a single cell, a very important parameter in biological and medical fields, has been attracting increasing attention. To date, methods developed to determine this permeability are either operation-complicated or time-consuming. Therefore, we developed a chip for automatically and rapidly determining the permeability of cells that integrates microfluidics and cell impedance analysis. The chip is designed to automatically identify a single cell, capture the cell, and record the volume change in that cell. We confirmed the abilities of single-cell identification and capture with the upper and lower voltage thresholds determined, validated the performance of the differential electrode design for accurate cell volume measurements, deduced the extracellular osmotic pressure change in the presence of a hypertonic solution according to fluorescence intensity, and demonstrated the single-cell volume change recorded by the chip. Then, the accuracy of the permeability determined with the chip was verified using HeLa cells. Finally, the permeability of human-induced pluripotent stem cells (hiPSCs) was determined to be 0.47 ± 0.03 μm/atm/min. Using the chip, the permeability can be determined within 5 min. This study provides insights for the new design of an automatic single-cell identification and capture chip for single cell–related studies. [ABSTRACT FROM AUTHOR]

Background: Schizotypal traits are considered as inheritable traits and the endophenotype for schizophrenia. A common variant in the NOTCH4 gene, rs204993, has been linked with schizophrenia, but the neural underpinnings are largely unknown. Methods: In present study, we compared the differences of brain functions between different genotypes of rs204993 and its relationship with schizotypal traits among 402 Chinese Han healthy volunteers. The brain function was evaluated with functional connectivity strength (FCS) using the resting-state functional magnetic resonance image(rs-fMRI). The schizotypal traits were measured by the schizotypal personality questionnaire (SPQ). Results: Our results showed that carriers with the AA genotype showed reduced FCS in the left occipital cortex when compared with carriers with the AG and GG genotypes, and the carriers with the AG genotype showed reduced FCS in the left occipital cortex when compared with carriers with the GG genotype. The FCS values in the left occipital lobe were negatively associated with the SPQ scores and its subscale scores within the carriers with the GG genotype, but not within the carriers with AA or AG genotype. Conclusion: Our results suggested that the common variant in the NOTCH4 gene, rs204993, modulates the function of the occipital cortex, which may contribute to schizotypal traits. These findings provide insight for genetic effects on schizotypal traits and its potential neural substrate. [ABSTRACT FROM AUTHOR]

Individuals with autism-like traits (ALT) belong to a subclinical group with similar social deficits as autism spectrum disorders (ASD). Their main social deficits include atypical eye contact and difficulty in understanding facial expressions, both of which are associated with an abnormality of the right posterior superior temporal sulcus (rpSTS). It is still undetermined whether it is possible to improve the social function of ALT individuals through noninvasive neural modulation. To this end, we randomly assigned ALT individuals into the real (n = 16) and sham (n = 16) stimulation groups. All subjects received five consecutive days of intermittent theta burst stimulation (iTBS) on the rpSTS. Eye tracking data and functional magnetic resonance imaging (fMRI) data were acquired on the first and sixth days. The real group showed significant improvement in emotion recognition accuracy after iTBS, but the change was not significantly larger than that in the sham group. Resting-state functional connectivity (rsFC) between the rpSTS and the left cerebellum significantly decreased in the real group than the sham group after iTBS. At baseline, rsFC in the left cerebellum was negatively correlated with emotion recognition accuracy. Our findings indicated that iTBS of the rpSTS could improve emotion perception of ALT individuals by modulating associated neural networks. This stimulation protocol could be a vital therapeutic strategy for the treatment of ASD. [ABSTRACT FROM AUTHOR]

Purpose: Spatial resolution is an important parameter for magnetic resonance imaging (MRI). High‐resolution MR images provide detailed information and benefit subsequent image analysis. However, higher resolution MR images come at the expense of longer scanning time and lower signal‐to‐noise ratios (SNRs). Using algorithms to improve image resolution can mitigate these limitations. Recently, some convolutional neural network (CNN)‐based super‐resolution (SR) algorithms have flourished on MR image reconstruction. However, most algorithms usually adopt deeper network structures to improve the performance. Methods: In this study, we propose a novel hybrid network (named HybridNet) to improve the quality of SR images by increasing the width of the network. Specifically, the proposed hybrid block combines a multipath structure and variant dense blocks to extract abundant features from low‐resolution images. Furthermore, we fully exploit the hierarchical features from different hybrid blocks to reconstruct high‐quality images. Results: All SR algorithms are evaluated using three MR image datasets and the proposed HybridNet outperformed the comparative methods with peak a signal‐to‐noise ratio (PSNR) of 42.12 ± 0.92 dB, 38.60 ± 2.46 dB, 35.17 ± 2.96 dB and a structural similarity index (SSIM) of 0.9949 ± 0.0015, 0.9892 ± 0.0034, 0.9740 ± 0.0064, respectively. Besides, our proposed network can reconstruct high‐quality images on an unseen MR dataset with PSNR of 33.27 ± 1.56 and SSIM of 0.9581 ± 0.0068. Conclusions: The results demonstrate that HybridNet can reconstruct high‐quality SR images from degraded MR images and has good generalization ability. It also can be leveraged to assist the task of image analysis or processing. [ABSTRACT FROM AUTHOR]

Purpose: Adenoid hypertrophy is a pathological hyperplasia of adenoids and may cause snoring, apnea, and impede breathing during sleep. In clinical practice, radiologists diagnose the severity of adenoid hypertrophy by measuring the ratio of adenoid width (A) to nasopharyngeal width (N) according to the lateral cephalogram, which indicates the locations of four keypoints. The entire diagnostic process is tedious and time‐consuming due to the acquisition of A and N. Thus, there is an urgent need to develop computer‐aided diagnostic tools for adenoid hypertrophy. Methods: In this paper, we first propose the use of deep learning to solve the problem of adenoid hypertrophy classification. Deep learning driven by big data has developed greatly in the image processing field. However, obtaining a large amount of training data is hard, making the application of deep learning to medical images more difficult. This paper proposes a keypoint localization method to incorporate more prior information to improve the performance of the model under limited data. Furthermore, we design a novel regularized term called VerticalLoss to capture the vertical relationship between keypoints to provide prior information to strengthen the network performance. Results: To evaluate the performance of our proposed method, we conducted experiments with a clinical dataset from the First Affiliated Hospital of Anhui Medical University consisting of a total of 688 patients. As our results show, we obtained a classification accuracy of 95.6%, a macro F1‐score of 0.957, and an average AN ratio error of 0.026. Furthermore, we obtained a macro F1‐score of 0.89, a classification accuracy of 94%, and an average AN ratio error of 0.027 while using only half of the data for training. Conclusions: The study shows that our proposed method can achieve satisfactory results in the task of adenoid hypertrophy classification. Our approach incorporates more prior information, which is especially important in the field of medical imaging, where it is difficult to obtain large amounts of training data. [ABSTRACT FROM AUTHOR]

Purpose: Adenoid hypertrophy is a pathological hyperplasia of adenoids and may cause snoring, apnea, and impede breathing during sleep. In clinical practice, radiologists diagnose the severity of adenoid hypertrophy by measuring the ratio of adenoid width (A) to nasopharyngeal width (N) according to the lateral cephalogram, which indicates the locations of four keypoints. The entire diagnostic process is tedious and time‐consuming due to the acquisition of A and N. Thus, there is an urgent need to develop computer‐aided diagnostic tools for adenoid hypertrophy. Methods: In this paper, we first propose the use of deep learning to solve the problem of adenoid hypertrophy classification. Deep learning driven by big data has developed greatly in the image processing field. However, obtaining a large amount of training data is hard, making the application of deep learning to medical images more difficult. This paper proposes a keypoint localization method to incorporate more prior information to improve the performance of the model under limited data. Furthermore, we design a novel regularized term called VerticalLoss to capture the vertical relationship between keypoints to provide prior information to strengthen the network performance. Results: To evaluate the performance of our proposed method, we conducted experiments with a clinical dataset from the First Affiliated Hospital of Anhui Medical University consisting of a total of 688 patients. As our results show, we obtained a classification accuracy of 95.6%, a macro F1‐score of 0.957, and an average AN ratio error of 0.026. Furthermore, we obtained a macro F1‐score of 0.89, a classification accuracy of 94%, and an average AN ratio error of 0.027 while using only half of the data for training. Conclusions: The study shows that our proposed method can achieve satisfactory results in the task of adenoid hypertrophy classification. Our approach incorporates more prior information, which is especially important in the field of medical imaging, where it is difficult to obtain large amounts of training data. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM)‐based multivariate pattern analysis (MVPA) has delivered promising performance in decoding specific task states based on functional magnetic resonance imaging (fMRI) of the human brain. Conventionally, the SVM‐MVPA requires careful feature selection/extraction according to expert knowledge. In this study, we propose a deep neural network (DNN) for directly decoding multiple brain task states from fMRI signals of the brain without any burden for feature handcrafts. We trained and tested the DNN classifier using task fMRI data from the Human Connectome Project's S1200 dataset (N = 1,034). In tests to verify its performance, the proposed classification method identified seven tasks with an average accuracy of 93.7%. We also showed the general applicability of the DNN for transfer learning to small datasets (N = 43), a situation encountered in typical neuroscience research. The proposed method achieved an average accuracy of 89.0 and 94.7% on a working memory task and a motor classification task, respectively, higher than the accuracy of 69.2 and 68.6% obtained by the SVM‐MVPA. A network visualization analysis showed that the DNN automatically detected features from areas of the brain related to each task. Without incurring the burden of handcrafting the features, the proposed deep decoding method can classify brain task states highly accurately, and is a powerful tool for fMRI researchers. [ABSTRACT FROM AUTHOR]

Background. Clinical and experimental data suggest that ultrasound stimulation (US) at acupoints can produce similar effective treatment compared to manual acupuncture (MA). Although the brain activation to MA at acupoints is investigated by numerous studies, the brain activation to US at acupoints remains unclear. Methods. In the present work, we employed task state functional magnetic resonance imaging (fMRI) to explore the human brain's activation to US and MA at ST 36 (Zusanli) which is one of the most commonly used acupoints in acupuncture-related studies. 16 healthy subjects underwent US and MA procedures in an interval of more than one week. On-off block design stimulation was used for the recording of fMRI-related brain patterns. Results. Both US and MA at ST 36 produced activations in somatosensory and limbic/paralimbic regions (postcentral gyrus, insula, middle prefrontal cortex, and anterior cingulate cortex). Only US at ST 36 produced a significant signal increase in the inferior parietal lobule and decrease in the posterior cingulate cortex, whereas MA at ST 36 produced a significant signal increase in the lentiform nucleus and cerebellum. Conclusions. Our results indicate that US may be a possible noninvasive alternative method to MA due to its similar activation patterns. [ABSTRACT FROM AUTHOR]

Modulating the temporoparietal junction (TPJ), especially the right counterpart, shows promises in enhancing social cognitive ability. However, it is ambiguous whether the functional lateralization of TPJ determines people's responsiveness to brain stimulation. Here, this issue is investigated with an individual difference approach. Forty‐five participants attended three sessions of transcranial direct current stimulation (tDCS) experiments and one neuroimaging session. The results support the symmetric mechanism of left and right TPJ stimulation. First, the left and right TPJ stimulation effect are comparable in the group‐level analysis. Second, the individual‐level analysis reveals that a less right‐lateralized TPJ is associated with a higher level of responsiveness. Participants could be classified into positive responders showing cognitive enhancement and negative responders showing cognitive impairment due to stimulation. The positive responders show weaker connectivity between bilateral TPJ and the medial prefrontal cortex, which mediates the prediction of offline responsiveness by the lateralization and the social‐related trait. These findings call for a better characterization and predictive models for whom tDCS should be used for, and highlight the necessity and feasibility of prestimulation screening. [ABSTRACT FROM AUTHOR]

• iTBS combined with MRI–guided neuronavigation have been proposed as a more preponderant treatment of schizophrenia • Modulation of L-DLPFC activity may be a potential option for alleviating symptoms of schizophrenia • iTBS group showed a significant superiority over sham group in remission of symptoms of schizophrenia • The clinical post-intervention effects of 2-weeks iTBS can maintain for nearly 2 months. [ABSTRACT FROM AUTHOR]

Ewing sarcoma (EwS) is an aggressive pediatric tumor treated with intensive cytotoxic chemotherapies. Overall survival for metastatic or relapsed disease is only 20–30%. Metformin has long been an attractive therapeutic option for EwS, but hypoxia limits its efficacy. Through a systematic integration of drug combination screening, bioinformatics analyses, functional and in vivo studies, and correlation with clinical outcome, we identified another known drug, imatinib that could augment the in vivo anti-tumor capacity of metformin by attenuating tumor hypoxic response. This drug combination regimen widely suppressed multiple dominant mechanisms in EwS genesis, growth, and metastasis, including key EWS-FLI1 downstream targets that converge into the PI3K/AKT/mTOR signaling pathway. In addition, the combination significantly enhanced inhibition on tumor cell proliferation by standard EwS chemotherapy drugs, including cyclophosphamide and ifosfamide. This suggests a potential clinical benefit of the metformin/imatinib combination by allowing the reduction in dose intensity of standard chemotherapy without compromising survival outcome and represents a potential faster track application for EwS patients. • A drug repositioning and combination screening identified seven synergistic drug combinations in inhibiting the viability of Ewing sarcoma cell lines under hypoxia. • The imatinib and metformin combination regimen has potent synergistic anti-tumor effect in Ewing sarcoma in vitro and in vivo models. • This combination suppresses dominant mechanisms in Ewing sarcoma including the key EWS-FLI1 downstream targets that are convergent into the PI3K/AKT/mTOR signaling pathway. • The drug combination-related 48 gene signature showed significant correlation with clinical outcomes in Ewing sarcoma, sarcoma, GBM, stage III-IV ovarian cancer and early stage breast cancer cohorts. [ABSTRACT FROM AUTHOR]

Social deficits are features of autism and highly heritable traits. A common variant in autism-related CNTNAP2 gene, rs2710102, has been linked with social performance, but the neural substrates are largely unknown. We investigated variations in social performance and functional connectivity (static and dynamic) in the subregions of right temporoparietal junction (RTPJ), a key node of brain social network, using resting-state magnetic resonance imaging (n  = 399) by genotype at rs2710102 in healthy volunteers. Social performance was evaluated using the social domain of the Autism-Spectrum Quotient (AQ-social; n  = 641) and fixation time on eye areas during an eye-tracking task (n  = 32). According to previous evidence that the A-allele is the risk allele for social dysfunction, we classified participants into GG and A-allele carriers (AA/AG) groups. The A-allele carriers showed poor social performance (high AQ-social and short fixation time on eye areas) compared with the GG carriers. In the A-allele carriers, decreased stationary functional connectivity between the orbitofrontal cortex and posterior RTPJ (pRTPJ), and decreased dynamic functional connectivity (dFC) between the medial prefrontal cortex (mPFC) and pRTPJ were observed. The fixation time at eye areas positively were correlated with the pRTPJ-mPFC dFC. These findings provided insight for genetic effect on social behavior and its potential neural substrate. [ABSTRACT FROM AUTHOR]

Purpose: The pathological mechanisms of constant exotropia (XT) are still not understood. This study aimed to critically investigate whether patients with XT express neuronal activity changes after the critical period of visual development and further explore how these alterations are associated with behavioral performance. Materials and methods: Fourteen patients with XT and 16 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (fMRI). The regional homogeneity (ReHo) method was used to evaluate spontaneous brain activities. The association between significantly altered mean ReHo values and behavioral performance was assessed using Pearson's correlation analysis. Results: Compared with HCs, the right secondary visual cortex (V2) in patients with XT exhibited increased ReHo values, whereas the left Brodmann area 47 (BA47) demonstrated decreased spontaneous ReHo values. In patients with XT, the correlation between the left BA47's mean ReHo value and duration of strabismus was positively significant. Conclusions: These findings indicate that patients with XT have severe neural dysfunction in the right V2 and left BA47, and pathological severity in the left BA47 is likely influenced by duration of ongoing strabismus. Therefore, these results may provide clinically important information toward understanding the underlying pathological mechanisms of XT and thus can be fundamental in future XT research. [ABSTRACT FROM AUTHOR]