Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.Magnetic resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain images.MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders.Typically,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention.However,early diagnosis of brain tumors is intricate,necessitating the use of computerized methods.This research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain tumors.The proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third step.The final step involves classification using the Support Vector Machine(SVM)classifier.The classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)database.The proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of 0.957.Additionally,this method exhibits a shorter processing time of 0.44 s compared to existing approaches.The performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and DSC.To enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.

Altered spontaneous brain activity patterns in hypertensive retinopathy using fractional amplitude of low-frequency fluctuations:a functional magnetic resonance imaging study

AIM:To study functional brain abnormalities in patients with hypertensive retinopathy(HR)and to discuss the pathophysiological mechanisms of HR by fractional amplitude of low-frequency fluctuations(fALFFs)method.METHODS:Twenty HR patients and 20 healthy controls(HCs)were respectively recruited.The age,gender,and educational background characteristics of the two groups were similar.After functional magnetic resonance imaging(fMRI)scanning,the subjects’spontaneous brain activity was evaluated with the fALFF method.Receiver operating characteristic(ROC)curve analysis was used to classify the data.Further,we used Pearson’s correlation analysis to explore the relationship between fALFF values in specific brain regions and clinical behaviors in patients with HR.RESULTS:The brain areas of the HR group with lower fALFF values than HCs were the right orbital part of the middle frontal gyrus(RO-MFG)and right lingual gyrus.In contrast,the values of fALFFs in the left middle temporal gyrus(MTG),left superior temporal pole(STP),left middle frontal gyrus(MFG),left superior marginal gyrus(SMG),left superior parietal lobule(SPL),and right supplementary motor area(SMA)were higher in the HR group.The results of a t-test showed that the average values of fALFFs were statistically significantly different in the HR group and HC group(P<0.001).The fALFF values of the left middle frontal gyrus in HR patients were positively correlated with anxiety scores(r=0.9232;P<0.0001)and depression scores(r=0.9682;P<0.0001).CONCLUSION:fALFF values in multiple brain regions of HR patients are abnormal,suggesting that these brain regions in HR patients may be dysfunctional,which may help to reveal the pathophysiological mechanisms of HR.

Is it a normal phenomenon for pediatric patients to have brain leptomeningeal contrast enhancement on 3-tesla magnetic resonance imaging?

Determining whether sevoflurane sedation in children leads to“pseudo”prominent leptomeningeal contrast enhancement(pLMCE)on 3 Tesla magnetic resonance imaging will help reduce overdiagnosis by radiologists and clarify the pathophysiological changes of pLMCE.

GPU-accelerated three-dimensional reconstruction method of the Compton camera and its application in radionuclide imaging

A novel and fast three-dimensional reconstruction method for a Compton camera and its performance in radionuclide imaging is proposed and analyzed in this study. The conical surface sampling back-projection method with scattering angle correction(CSS-BP-SC) can quickly perform the back-projection process of the Compton cone and can be used to precompute the list-mode maximum likelihood expectation maximization(LM-MLEM). A dedicated parallel architecture was designed for the graphics processing unit acceleration of the back-projection and iteration stage of the CSS-BP-SC-based LM-MLEM. The imaging results of the two-point source Monte Carlo(MC) simulation demonstrate that by analyzing the full width at half maximum along the three coordinate axes, the CSS-BP-SC-based LM-MLEM can obtain imaging results comparable to those of the traditional reconstruction algorithm, that is, the simple back-projection-based LM-MLEM. The imaging results of the mouse phantom MC simulation and experiment demonstrate that the reconstruction results obtained by the proposed method sufficiently coincide with the set radioactivity distribution, and the speed increased by more than 664 times compared to the traditional reconstruction algorithm in the mouse phantom experiment. The proposed method will further advance the imaging applications of Compton cameras.

Imaging assessment of photosensitizer emission induced by radionuclide-derived Cherenkov radiation using charge-coupled device optical imaging and long-pass filters

BACKGROUND Radionuclides produce Cherenkov radiation(CR),which can potentially activate photosensitizers(PSs)in phototherapy.Several groups have studied Cherenkov energy transfer to PSs using optical imaging;however,cost-effectively identifying whether PSs are excited by radionuclide-derived CR and detecting fluorescence emission from excited PSs remain a challenge.Many laboratories face the need for expensive dedicated equipment.AIM To cost-effectively confirm whether PSs are excited by radionuclide-derived CR and distinguish fluorescence emission from excited PSs.METHODS The absorbance and fluorescence spectra of PSs were measured using a microplate reader and fluorescence spectrometer to examine the photo-physical properties of PSs.To mitigate the need for expensive dedicated equipment and achieve the aim of the study,we developed a method that utilizes a chargecoupled device optical imaging system and appropriate long-pass filters of different wavelengths(manual sequential application of long-pass filters of 515,580,645,700,750,and 800 nm).Tetrakis(4-carboxyphenyl)porphyrin(TCPP)was utilized as a model PS.Different doses of copper-64(^(64)CuCl_(2))(4,2,and 1 mCi)were used as CR-producing radionuclides.Imaging and data acquisition were performed 0.5 h after sample preparation.Differential image analysis was conducted by using ImageJ software(National Institutes of Health)to visually evaluate TCPP fluorescence.RESULTS The maximum absorbance of TCPP was at 390-430 nm,and the emission peak was at 670 nm.The CR and CRinduced TCPP emissions were observed using the optical imaging system and the high-transmittance long-pass filters described above.The emission spectra of TCPP with a peak in the 645-700 nm window were obtained by calculation and subtraction based on the serial signal intensity(total flux)difference between^(64)CuCl_(2)+TCPP and^(64)CuCl_(2).Moreover,the differential fluorescence images of TCPP were obtained by subtracting the^(64)CuCl_(2)image from the^(64)CuCl_(2)+TCPP image.The experimental results considering different^(64)CuCl_(2)doses showed a dosedependent trend.These results demonstrate that a bioluminescence imaging device coupled with different longpass filters and subtraction image processing can confirm the emission spectra and differential fluorescence images of CR-induced TCPP.CONCLUSION This simple method identifies the PS fluorescence emission generated by radionuclide-derived CR and can contribute to accelerating the development of Cherenkov energy transfer imaging and the discovery of new PSs.

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

Structural and functional alterations in the brains of patients with anisometropic and strabismic amblyopia:a systematic review of magnetic resonance imaging studies

Amblyopia is the most common cause of vision loss in children and can persist into adulthood in the absence of effective intervention.Previous clinical and neuroimaging studies have suggested that the neural mechanisms underlying strabismic amblyopia and anisometropic amblyopia may be different.Therefore,we performed a systematic review of magnetic resonance imaging studies investigating brain alterations in patients with these two subtypes of amblyopia;this study is registered with PROSPERO(registration ID:CRD42022349191).We searched three online databases(PubMed,EMBASE,and Web of Science) from inception to April 1,2022;39 studies with 633 patients(324patients with anisometropic amblyo pia and 309 patients with strabismic amblyopia) and 580 healthy controls met the inclusion criteria(e.g.,case-control designed,pee r-reviewed articles) and were included in this review.These studies highlighted that both strabismic amblyopia and anisometropic amblyopia patients showed reduced activation and distorted topological cortical activated maps in the striate and extrastriate co rtices during tas k-based functional magnetic resonance imaging with spatial-frequency stimulus and retinotopic representations,respectively;these may have arisen from abnormal visual experiences.Compensations for amblyopia that are reflected in enhanced spontaneous brain function have been reported in the early visual cortices in the resting state,as well as reduced functional connectivity in the dorsal pathway and structural connections in the ventral pathway in both anisometro pic amblyopia and strabismic amblyopia patients.The shared dysfunction of anisometro pic amblyopia and strabismic amblyopia patients,relative to controls,is also chara cterized by reduced spontaneous brain activity in the oculomotor co rtex,mainly involving the frontal and parietal eye fields and the cerebellu m;this may underlie the neural mechanisms of fixation instability and anomalous saccades in amblyopia.With regards to specific alterations of the two forms of amblyo pia,anisometropic amblyo pia patients suffer more microstructural impairments in the precortical pathway than strabismic amblyopia patients,as reflected by diffusion tensor imaging,and more significant dysfunction and structural loss in the ventral pathway.Strabismic amblyopia patients experience more attenuation of activation in the extrastriate co rtex than in the striate cortex when compared to anisometropic amblyopia patients.Finally,brain structural magnetic resonance imaging alterations tend to be lateralized in the adult anisometropic amblyopia patients,and the patterns of brain alterations are more limited in amblyopic adults than in childre n.In conclusion,magnetic resonance imaging studies provide important insights into the brain alterations underlying the pathophysiology of amblyopia and demonstrate common and specific alte rations in anisometropic amblyo pia and strabismic amblyopia patients;these alterations may improve our understanding of the neural mechanisms underlying amblyopia.

Recent progress in the applications of presynaptic dopaminergic positron emission tomography imaging in parkinsonism

Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of parkinsonism.This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism.We conducted a thorough literature search using reputable databases such as PubMed and Web of Science.Selection criteria involved identifying peer-reviewed articles published within the last 5 years,with emphasis on their relevance to clinical applications.The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis.Moreover,when employed in conjunction with other imaging modalities and advanced analytical methods,presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker.This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion.In summary,the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials,ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.

Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging

Central nervous system abnormalities in fetuses are fairly common,happening in 0.1%to 0.2%of live births and in 3%to 6%of stillbirths.So initial detection and categorization of fetal Brain abnormalities are critical.Manually detecting and segmenting fetal brain magnetic resonance imaging(MRI)could be timeconsuming,and susceptible to interpreter experience.Artificial intelligence(AI)algorithms and machine learning approaches have a high potential for assisting in the early detection of these problems,improving the diagnosis process and follow-up procedures.The use of AI and machine learning techniques in fetal brain MRI was the subject of this narrative review paper.Using AI,anatomic fetal brain MRI processing has investigated models to predict specific landmarks and segmentation automatically.All gestation age weeks(17-38 wk)and different AI models(mainly Convolutional Neural Network and U-Net)have been used.Some models'accuracy achieved 95%and more.AI could help preprocess and postprocess fetal images and reconstruct images.Also,AI can be used for gestational age prediction(with one-week accuracy),fetal brain extraction,fetal brain segmentation,and placenta detection.Some fetal brain linear measurements,such as Cerebral and Bone Biparietal Diameter,have been suggested.Classification of brain pathology was studied using diagonal quadratic discriminates analysis,Knearest neighbor,random forest,naive Bayes,and radial basis function neural network classifiers.Deep learning methods will become more powerful as more large-scale,labeled datasets become available.Having shared fetal brain MRI datasets is crucial because there aren not many fetal brain pictures available.Also,physicians should be aware of AI's function in fetal brain MRI,particularly neuroradiologists,general radiologists,and perinatologists.

Transformation of MRI Images to Three-Level Color Spaces for Brain Tumor Classification Using Deep-Net

In the domain ofmedical imaging,the accurate detection and classification of brain tumors is very important.This study introduces an advanced method for identifying camouflaged brain tumors within images.Our proposed model consists of three steps:Feature extraction,feature fusion,and then classification.The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques,using the ResNet50 Convolutional Neural Network(CNN)architecture.So the focus is to extract robust feature fromMRI images,particularly emphasizingweighted average features extracted fromthe first convolutional layer renowned for their discriminative power.To enhance model robustness,we introduced a novel feature fusion technique based on the Marine Predator Algorithm(MPA),inspired by the hunting behavior of marine predators and has shown promise in optimizing complex problems.The proposed methodology can accurately classify and detect brain tumors in camouflage images by combining the power of color transformations,deep learning,and feature fusion via MPA,and achieved an accuracy of 98.72%on a more complex dataset surpassing the existing state-of-the-art methods,highlighting the effectiveness of the proposed model.The importance of this research is in its potential to advance the field ofmedical image analysis,particularly in brain tumor diagnosis,where diagnoses early,and accurate classification are critical for improved patient results.

Does sevoflurane sedation in pediatric patients lead to“pseudo”leptomeningeal enhancement in the brain on 3 Tesla magnetic resonance imaging?

BACKGROUND Prominent leptomeningeal contrast enhancement(LMCE)in the brain is observed in some pediatric patients during sedation for imaging.However,based on clinical history and cerebrospinal fluid analysis,the patients are not acutely ill and do not exhibit meningeal signs.Our study determined whether sevoflurane inhalation in pediatric patients led to this pattern of‘pseudo’LMCE(pLMCE)on 3 Tesla magnetic resonance imaging(MRI).AIM To highlight the significance of pLMCE in pediatric patients undergoing enhanced brain MRI under sedation to avoid misinterpretation in reports.METHODS A retrospective cross-sectional evaluation of pediatric patients between 0-8 years of age was conducted.The patients underwent enhanced brain MRI under inhaled sevoflurane.The LMCE grade was determined by two radiologists,and interobserver variability of the grade was calculated using Cohen’s kappa.The LMCE grade was correlated with duration of sedation,age and weight using the Spearman rho rank correlation.RESULTS A total of 63 patients were included.Fourteen(22.2%)cases showed mild LMCE,48(76.1%)cases showed moderate LMCE,and 1 case(1.6%)showed severe LMCE.We found substantial agreement between the two radiologists in detection of pLMCE on post-contrast T1 imaging(kappa value=0.61;P<0.001).Additionally,we found statistically significant inverse and moderate correlations between patient weight and age.There was no correlation between duration of sedation and pLMCE.CONCLUSION pLMCE is relatively common on post-contrast spin echo T1-weighted MRI of pediatric patients sedated by sevoflurane due to their fragile and immature vasculature.It should not be misinterpreted for meningeal pathology.Knowing pertinent clinical history of the child is an essential prerequisite to avoid radiological overcalling and the subsequent burden of additional investigations.

Quantitative ultrasound brain imaging with multiscale deconvolutional waveform inversion

High-resolution images of human brain are critical for monitoring the neurological conditions in a portable and safe manner.Sound speed mapping of brain tissues provides unique information for such a purpose.In addition,it is particularly important for building digital human acoustic models,which form a reference for future ultrasound research.Conventional ultrasound modalities can hardly image the human brain at high spatial resolution inside the skull due to the strong impedance contrast between hard tissue and soft tissue.We carry out numerical experiments to demonstrate that the time-domain waveform inversion technique,originating from the geophysics community,is promising to deliver quantitative images of human brains within the skull at a sub-millimeter level by using ultra-sound signals.The successful implementation of such an approach to brain imaging requires the following items:signals of sub-megahertz frequencies transmitting across the inside of skull,an accurate numerical wave equation solver simulating the wave propagation,and well-designed inversion schemes to reconstruct the physical parameters of targeted model based on the optimization theory.Here we propose an innovative modality of multiscale deconvolutional waveform inversion that improves ultrasound imaging resolution,by evaluating the similarity between synthetic data and observed data through using limited length Wiener filter.We implement the proposed approach to iteratively update the parametric models of the human brain.The quantitative imaging method paves the way for building the accurate acoustic brain model to diagnose associated diseases,in a potentially more portable,more dynamic and safer way than magnetic resonance imaging and x-ray computed tomography.

3D Kronecker Convolutional Feature Pyramid for Brain Tumor Semantic Segmentation in MR Imaging

Brain tumor significantly impacts the quality of life and changes everything for a patient and their loved ones.Diagnosing a brain tumor usually begins with magnetic resonance imaging(MRI).The manual brain tumor diagnosis from the MRO images always requires an expert radiologist.However,this process is time-consuming and costly.Therefore,a computerized technique is required for brain tumor detection in MRI images.Using the MRI,a novel mechanism of the three-dimensional(3D)Kronecker convolution feature pyramid(KCFP)is used to segment brain tumors,resolving the pixel loss and weak processing of multi-scale lesions.A single dilation rate was replaced with the 3D Kronecker convolution,while local feature learning was performed using the 3D Feature Selection(3DFSC).A 3D KCFP was added at the end of 3DFSC to resolve weak processing of multi-scale lesions,yielding efficient segmentation of brain tumors of different sizes.A 3D connected component analysis with a global threshold was used as a post-processing technique.The standard Multimodal Brain Tumor Segmentation 2020 dataset was used for model validation.Our 3D KCFP model performed exceptionally well compared to other benchmark schemes with a dice similarity coefficient of 0.90,0.80,and 0.84 for the whole tumor,enhancing tumor,and tumor core,respectively.Overall,the proposed model was efficient in brain tumor segmentation,which may facilitate medical practitioners for an appropriate diagnosis for future treatment planning.

Machine Learning-Based Models for Magnetic Resonance Imaging(MRI)-Based Brain Tumor Classification

In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans.Automatic(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic applications.Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio.The brain tumor is one of the most common causes of death.Researchers have already proposed various models for the classification and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classification process with improved efficiency.However,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving accuracy.On the Kaggle dataset,these strategies are tested using classification accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation technique.The results show that SVM outperforms other algorithms,with 95.3%accuracy.

EVALUATION OF RADIONUCLIDE BONE IMAGING FOR SKELETAL DISEASE

Whole body bone scan imaging of 99mTc-MDP was performed in 80 casesfrom Sept 1991 to Feb 1992. Among them 20 patients showed negtive bone imaging and56 patients showed positive bone imaging. There were false-positive bone imaging in 4 pa-tients. Bone scan imaging has been regarded as a useful method in the early diagnosis ofshelatal disease, especially in old patients with bone metastasis. But the final confirmationof malignancy should be still cautious.

Role of radionuclide imaging for diagnosis of device and prosthetic valve infections

Cardiovascular implantable electronic device(CIED) infection and prosthetic valve endocarditis(PVE) remain a diagnostic challenge.Cardiac imaging plays an important role in the diagnosis and management of patients with CIED infection or PVE.Over the past few years,cardiac radionuclide imaging has gained a key role in the diagnosis of these patients,and in assessing the need for surgery,mainly in the most difficult cases.Both ^(18)F-fluorodeoxyglucose positron emission tomography/computed tomography(^(18)F-FDG PET/CT) and radiolabelled white blood cell single-photon emission computed tomography/computed tomography(WBC SPECT/CT) have been studied in these situations.In their 2015 guidelines for the management of infective endocarditis,the European Society of Cardiology incorporated cardiac nuclear imaging as part of their diagnostic algorithm for PVE,but not CIED infection since the data were judged insufficient at the moment.This article reviews the actual knowledge and recent studies on the use of ^(18)F-FDG PET/CT and WBC SPECT/CT in the context of CIED infection and PVE,and describes the technical aspects of cardiac radionuclide imaging.It also discusses their accepted and potential indications for the diagnosis and management of CIED infection and PVE,the limitations of these tests,and potential areas of future research.

RADIONUCLIDE WHOLE BODY BONE IMAGING IN THE DIAGNOSIS OF SKELETAL METASTASES

Of 628 patients with extra-osseous malignancies diagnosed by surgery and/or pathology, 207 (33.0%) were identified as having skeletal metastasis by bone imaging. There was statistical significant difference in the incidence of metastasis in different malignancies (P<0.02). The metastatic rates of nasopharyn-geal, lung, prostate and breast cancers were higher than gastrointestinal, kidney, and other malignancies. There was significant differences in the different sites of skeletal metastasis (P<0.01). They were thorax, spine, pelvis, limbs and skull in order of incidence. Solitary metastatic rate was 15.9%. Biopsy is advised for patients suspected to have metastatic disease but with only one single 'hot spot' in skeletal imaging, particularly in the rib.

Highly Sensitive MoS_2–Indocyanine Green Hybrid for Photoacoustic Imaging of Orthotopic Brain Glioma at Deep Site

Photoacoustic technology in combination with molecular imaging is a highly effective method for accurately diagnosing brain glioma. For glioma detection at a deeper site, contrast agents with higher photoacoustic imaging sensitivity are needed. Herein, we report a MoS_2–ICG hybrid with indocyanine green(ICG) conjugated to the surface of MoS_2 nanosheets. The hybrid significantly enhanced photoacoustic imaging sensitivity compared to MoS_2 nanosheets. This conjugation results in remarkably high optical absorbance across a broad near-infrared spectrum, redshifting of the ICG absorption peak and photothermal/photoacoustic conversion efficiency enhancement of ICG. A tumor mass of 3.5 mm beneath the mouse scalp was clearly visualized by using MoS_2–ICG as a contrast agent for the in vivo photoacoustic imaging of orthotopic glioma, which is nearly twofold deeper than the tumors imaged in our previous report using MoS_2 nanosheet. Thus, combined with its good stability and high biocompatibility, the MoS_2–ICG hybrid developed in this study has a great potential for high-efficiency tumor molecular imaging in translational medicine.

Acupuncture at Waiguan(SJ5) and sham points influences activation of functional brain areas of ischemic stroke patients: a functional magnetic resonance imaging study

Most studies addressing the specificity of meridians and acupuncture points have focused mainly on the different neural effects of acupuncture at different points in healthy individuals. This study examined the effects of acupuncture on brain function in a pathological context. Sixteen patients with ischemic stroke were randomly assigned to true point group （true acupuncture at right Waiguan （SJ5）） and sham point group （sham acupuncture）. Results of functional magnetic resonance imaging revealed activation in right parietal lobe （Brodmann areas 7 and 19）, the right temporal lobe （Brodmann area 39）, the right limbic lobe （Brodmann area 23） and bilateral oc-cipital lobes （Brodmann area 18）. Furthermore, inhibition of bilateral frontal lobes （Brodmann area 4, 6, and 45）, right parietal lobe （Brodmann areas 1 and 5） and left temporal lobe （Brodmann area 21 ） were observed in the true point group. Activation in the precuneus of right parietal lobe （Brodmann area 7） and inhibition of the left superior frontal gyrus （Brodmann area 10） was observed in the sham group. Compared with sham acupuncture, acupuncture at Waiguan in stroke patients inhibited Brodmann area 5 on the healthy side. Results indicated that the altered specificity of sensation-associated cortex （Brodmann area 5） is possibly associated with a central mechanism of acupuncture at Waiguan for stroke patients.