Two-photon live imaging of direct glia-to-neuron conversion in the mouse cortex

Over the past decade,a growing number of studies have reported transcription factor-based in situ reprogramming that can directly conve rt endogenous glial cells into functional neurons as an alternative approach for n euro regeneration in the adult mammalian central ne rvous system.Howeve r,many questions remain regarding how a terminally differentiated glial cell can transform into a delicate neuron that forms part of the intricate brain circuitry.In addition,concerns have recently been raised around the absence of astrocyte-to-neuron conversion in astrocytic lineage-tra cing mice.In this study,we employed repetitive two-photon imaging to continuously capture the in situ astrocyte-to-neuron conversion process following ecto pic expression of the neural transcription factor NeuroD1 in both prolife rating reactive astrocytes and lineage-tra ced astrocytes in the mouse cortex.Time-lapse imaging over several wee ks revealed the ste p-by-step transition from a typical astrocyte with numero us short,tapered branches to a typical neuro n with a few long neurites and dynamic growth cones that actively explored the local environment.In addition,these lineage-converting cells were able to migrate ra dially or to ngentially to relocate to suitable positions.Furthermore,two-photon Ca2+imaging and patch-clamp recordings confirmed that the newly generated neuro ns exhibited synchronous calcium signals,repetitive action potentials,and spontaneous synaptic responses,suggesting that they had made functional synaptic connections within local neural circuits.In conclusion,we directly visualized the step-by-step lineage conversion process from astrocytes to functional neurons in vivo and unambiguously demonstrated that adult mammalian brains are highly plastic with respect to their potential for neuro regeneration and neural circuit reconstruction.

In vivo imaging of the neuronal response to spinal cord injury:a narrative review

Deciphering the neuronal response to injury in the spinal cord is essential for exploring treatment strategies for spinal cord injury(SCI).However,this subject has been neglected in part because appropriate tools are lacking.Emerging in vivo imaging and labeling methods offer great potential for observing dynamic neural processes in the central nervous system in conditions of health and disease.This review first discusses in vivo imaging of the mouse spinal cord with a focus on the latest imaging techniques,and then analyzes the dynamic biological response of spinal cord sensory and motor neurons to SCI.We then summarize and compare the techniques behind these studies and clarify the advantages of in vivo imaging compared with traditional neuroscience examinations.Finally,we identify the challenges and possible solutions for spinal cord neuron imaging.

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.

Topic highlight on texture and color enhancement imaging in gastrointestinal diseases

Olympus Corporation developed texture and color enhancement imaging(TXI)as a novel image-enhancing endoscopic technique.This topic highlights a series of hot-topic articles that investigated the efficacy of TXI for gastrointestinal disease identification in the clinical setting.A randomized controlled trial demonstrated improvements in the colorectal adenoma detection rate(ADR)and the mean number of adenomas per procedure(MAP)of TXI compared with those of white-light imaging(WLI)observation(58.7%vs 42.7%,adjusted relative risk 1.35,95%CI:1.17-1.56;1.36 vs 0.89,adjusted incident risk ratio 1.48,95%CI:1.22-1.80,respectively).A cross-over study also showed that the colorectal MAP and ADR in TXI were higher than those in WLI(1.5 vs 1.0,adjusted odds ratio 1.4,95%CI:1.2-1.6;58.2%vs 46.8%,1.5,1.0-2.3,respectively).A randomized controlled trial demonstrated non-inferiority of TXI to narrow-band imaging in the colorectal mean number of adenomas and sessile serrated lesions per procedure(0.29 vs 0.30,difference for non-inferiority-0.01,95%CI:-0.10 to 0.08).A cohort study found that scoring for ulcerative colitis severity using TXI could predict relapse of ulcerative colitis.A cross-sectional study found that TXI improved the gastric cancer detection rate compared to WLI(0.71%vs 0.29%).A cross-sectional study revealed that the sensitivity and accuracy for active Helicobacter pylori gastritis in TXI were higher than those of WLI(69.2%vs 52.5%and 85.3%vs 78.7%,res-pectively).In conclusion,TXI can improve gastrointestinal lesion detection and qualitative diagnosis.Therefore,further studies on the efficacy of TXI in clinical practice are required.

Self-confocal NIR-II fluorescence microscopy for multifunctional in vivo imaging

Fluorescence imaging in the second near-infrared window(NIR-II,900–1880 nm)with less scattering background in biological tissues has been combined with the confocal microscopic system for achieving deep in vivo imaging with high spatial resolution.However,the traditional NIR-IIfluorescence confocal microscope with separate excitation focus and detection pinhole makes it possess low confocal e±ciency,as well as di±cultly to adjust.Two types of upgraded NIR-IIfluorescence confocal microscopes,sharing the same pinhole by excitation and emission focus,leading to higher confocal e±ciency,are built in this work.One type is-ber-pinhole-based confocal microscope applicable to CW laser excitation.It is constructed forfluorescence intensity imaging with large depth,high stabilization and low cost,which could replace multiphotonfluorescence microscopy in some applications(e.g.,cerebrovascular and hepatocellular imaging).The other type is air-pinhole-based confocal microscope applicable to femtosecond(fs)laser excitation.It can be employed not only for NIR-IIfluorescence intensity imaging,but also for multi-channelfluorescence lifetime imaging to recognize different structures with similarfluorescence spectrum.Moreover,it can be facilely combined with multiphotonfluorescence microscopy.A single fs pulsed laser is utilized to achieve up-conversion(visible multiphotonfluorescence)and down-conversion(NIR-II one-photonfluorescence)excitation simultaneously,extending imaging spectral channels,and thus facilitates multi-structure and multi-functional observation.

A synthetic diagnostics platform for microwave imaging diagnostics in tokamaks

Interpreting experimental diagnostics data in tokamaks,while considering non-ideal effects,is challenging due to the complexity of plasmas.To address this challenge,a general synthetic diagnostics(GSD)platform has been established that facilitates microwave imaging reflectometry and electron cyclotron emission imaging.This platform utilizes plasma profiles as input and incorporates the finite-difference time domain,ray tracing and the radiative transfer equation to calculate the propagation of plasma spontaneous radiation and the external electromagnetic field in plasmas.Benchmark tests for classical cases have been conducted to verify the accuracy of every core module in the GSD platform.Finally,2D imaging of a typical electron temperature distribution is reproduced by this platform and the results are consistent with the given real experimental data.This platform also has the potential to be extended to 3D electromagnetic field simulations and other microwave diagnostics such as cross-polarization scattering.

Activatable fluorescent probes for imaging and diagnosis of rheumatoid arthritis

Rheumatoid arthritis(RA)is a systemic autoimmune disease that is primarily manifested as synovitis and polyarticular opacity and typically leads to serious joint damage and irreversible disability,thus adversely affecting locomotion ability and life quality.Consequently,good prognosis heavily relies on the early diagnosis and effective therapeutic monitoring of RA.Activatable fluorescent probes play vital roles in the detection and imaging of biomarkers for disease diagnosis and in vivo imaging.Herein,we review the fluorescent probes developed for the detection and imaging of RA biomarkers,namely reactive oxygen/nitrogen species(hypochlorous acid,peroxynitrite,hydroxyl radical,nitroxyl),pH,and cysteine,and address the related challenges and prospects to inspire the design of novel fluorescent probes and the improvement of their performance in RA studies.

Microwave-induced thermoacoustic elastic imaging:A simulation study

Microwave-induced thermoacoustic imaging(MTI)has the advantages of high resolution,high contrast,non-ionization,and non-invasive.Recently,MTI was used in the¯eld of breast cancer screening.In this paper,based on the¯nite element method(FEM)and COMSOL Multiphysics software,a three-dimensional breast cancer model suitable for exploring the MTI process is proposed to investigate the in°uence of Young's modulus(YM)of breast cancer tissue on MTI.It is found that the process of electromagnetic heating and initial pressure generation of the entire breast tissue is earlier in time than the thermal expansion process.Besides,compared with normal breast tissue,tumor tissue has a greater temperature rise,displacement,and pressure rise.In particular,YM of the tumor is related to the speed of thermal expansion.In particular,the larger the YM of the tumor is,the higher the heating and contraction frequency is,and the greater the maximum pressure is.Di®erent Young's moduli correspond to di®erent thermoacoustic signal spectra.In MTI,this study can be used to judge di®erent degrees of breast cancer based on elastic imaging.In addition,this study is helpful in exploring the possibility of microwave-induced thermoacoustic elastic imaging(MTAE).

Efficient single-pixel imaging encrypted transmission based on 3D Arnold transformation

Single-pixel imaging(SPI)can transform 2D or 3D image data into 1D light signals,which offers promising prospects for image compression and transmission.However,during data communication these light signals in public channels will easily draw the attention of eavesdroppers.Here,we introduce an efficient encryption method for SPI data transmission that uses the 3D Arnold transformation to directly disrupt 1D single-pixel light signals and utilizes the elliptic curve encryption algorithm for key transmission.This encryption scheme immediately employs Hadamard patterns to illuminate the scene and then utilizes the 3D Arnold transformation to permutate the 1D light signal of single-pixel detection.Then the transformation parameters serve as the secret key,while the security of key exchange is guaranteed by an elliptic curve-based key exchange mechanism.Compared with existing encryption schemes,both computer simulations and optical experiments have been conducted to demonstrate that the proposed technique not only enhances the security of encryption but also eliminates the need for complicated pattern scrambling rules.Additionally,this approach solves the problem of secure key transmission,thus ensuring the security of information and the quality of the decrypted images.

Quantification of the adulteration concentration of palm kernel oil in virgin coconut oil using near-infrared hyperspectral imaging

The adulteration concentration of palm kernel oil(PKO)in virgin coconut oil(VCO)was quantified using near-infrared(NIR)hyperspectral imaging.Nowadays,some VCO is adulterated with lower-priced PKO to reduce production costs,which diminishes the quality of the VCO.This study used NIR hyperspectral imaging in the wavelength region 900-1,650 nm to create a quantitative model for the detection of PKO contaminants(0-100%)in VCO and to develop predictive mapping.The prediction equation for the adulteration of VCO with PKO was constructed using the partial least squares regression method.The best predictive model was pre-processed using the standard normal variate method,and the coefficient of determination of prediction was 0.991,the root mean square error of prediction was 2.93%,and the residual prediction deviation was 10.37.The results showed that this model could be applied for quantifying the adulteration concentration of PKO in VCO.The prediction adulteration concentration mapping of VCO with PKO was created from a calibration model that showed the color level according to the adulteration concentration in the range of 0-100%.NIR hyperspectral imaging could be clearly used to quantify the adulteration of VCO with a color level map that provides a quick,accurate,and non-destructive detection method.

Development of a 2D spatial displacement estimation method for turbulence velocimetry of the gas puff imaging system on EAST

A gas puff imaging(GPI)diagnostic has been developed and operated on EAST since 2012,and the time-delay estimation(TDE)method is used to derive the propagation velocity of fluctuations from the two-dimensional GPI data.However,with the TDE method it is difficult to analyze the data with fast transient events,such as edge-localized mode(ELM).Consequently,a method called the spatial displacement estimation(SDE)algorithm is developed to estimate the turbulence velocity with high temporal resolution.Based on the SDE algorithm,we make some improvements,including an adaptive median filter and super-resolution technology.After the development of the algorithm,a straight-line movement and a curved-line movement are used to test the accuracy of the algorithm,and the calculated speed agrees well with preset speed.This SDE algorithm is applied to the EAST GPI data analysis,and the derived propagation velocity of turbulence is consistent with that from the TDE method,but with much higher temporal resolution.

A Novel Sensing Imaging Equipment Under Extremely Dim Light for Blast Furnace Burden Surface:Starlight High-Temperature Industrial Endoscope

Blast furnace(BF)burden surface contains the most abundant,intuitive and credible smelting information and acquiring high-definition and high-brightness optical images of which is essential to realize precise material charging control,optimize gas flow distribution and improve ironmaking efficiency.It has been challengeable to obtain high-quality optical burden surface images under high-temperature,high-dust,and extremelydim(less than 0.001 Lux)environment.Based on a novel endoscopic sensing detection idea,a reverse telephoto structure starlight imaging system with large field of view and large aperture is designed.Combined with a water-air dual cooling intelligent self-maintenance protection device and the imaging system,a starlight high-temperature industrial endoscope is developed to obtain clear optical burden surface images stably under the harsh environment.Based on an endoscope imaging area model,a material flow trajectory model and a gas-dust coupling distribution model,an optimal installation position and posture configuration method for the endoscope is proposed,which maximizes the effective imaging area and ensures large-area,safe and stable imaging of the device in a confined space.Industrial experiments and applications indicate that the proposed method obtains clear and reliable large-area optical burden surface images and reveals new BF conditions,providing key data support for green iron smelting.

Laser spectroscopy imaging technique coupled withnanomaterials for cancer diagnosis: A review

Laser spectroscopic imaging techniques have received tremendous attention in the-eld of cancer diagnosis due to their high sensitivity,high temporal resolution,and short acquisition time.However,the limited tissue penetration of the laser is still a challenge for the in vivo diagnosis of deep-seated lesions.Nanomaterials have been universally integrated with spectroscopic imaging techniques for deeper cancer diagnosis in vivo.The components,morphology,and sizes of nanomaterials are delicately designed,which could realize cancer diagnosis in vivo or in situ.Considering the enhanced signal emitting from the nanomaterials,we emphasized their combination with spectroscopic imaging techniques for cancer diagnosis,like the surface-enhanced Raman scattering(SERS),photoacoustic,fluorescence,and laser-induced breakdown spectroscopy(LIBS).Applications ofthe above spectroscopic techniques offer new prospectsfor cancer diagnosis.

NIR-II fluorescence imaging in liver tumor surgery: A narrative review

In liver tumor surgery,the recognition of tumor margin and radical resection of microcancer focis have always been the crucial points to reduce postoperative recurrence of tumor.However,naked-eye inspection and palpation have limited effectiveness in identifying tumor boundaries,and traditional imaging techniques cannot consistently locate tumors in real time.As an intraoperative real-time navigation imaging method,NIRfluorescence imaging has been extensively studied for its simplicity,reliable safety,and superior sensitivity,and is expected to improve the accuracy of liver tumor surgery.In recent years,the research focus of NIRfluorescence has gradually shifted from the-rst near-infrared window(NIR-I,700–900 nm)to the second near-infrared window(NIR-II,1000–1700 nm).Fluorescence imaging in NIR-II reduces the scattering effect of deep tissue,providing a preferable detection depth and spatial resolution while signi-cantly eliminating liver autofluorescence background to clarify tumor margin.Developingfluorophores combined with tumor antibodies will further improve the precision offluorescence-guided surgical navigation.With the development of a bunch offluorophores with phototherapy ability,NIR-II can integrate tumor detection and treatment to explore a new therapeutic strategy for liver cancer.Here,we review the recent progress of NIR-IIfluorescence technology in liver tumor surgery and discuss its challenges and potential development direction.

Development of a toroidal soft x-ray imaging system and application for investigating three-dimensional plasma on J-TEXT

A toroidal soft x-ray imaging(T-SXRI)system has been developed to investigate threedimensional(3D)plasma physics on J-TEXT.This T-SXRI system consists of three sets of SXR arrays.Two sets are newly developed and located on the vacuum chamber wall at toroidal positionsφof 126.4°and 272.6°,respectively,while one set was established previously atφ=65.50.Each set of SXR arrays consists of three arrays viewing the plasma poloidally,and hence can be used separately to obtain SXR images via the tomographic method.The sawtooth precursor oscillations are measured by T-SXRI,and the corresponding images of perturbative SXR signals are successfully reconstructed at these three toroidal positions,hence providing measurement of the 3D structure of precursor oscillations.The observed 3D structure is consistent with the helical structure of the m/n=1/1 mode.The experimental observation confirms that the T-SXRI system is able to observe 3D structures in the J-TEXT plasma.

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

The present study aimed to explore the potential of artificial intelligence(AI)methodology based on magnetic resonance(MR)images to aid in the management of prostate cancer(PCa).To this end,we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics,thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa.First,we found that,in the included studies of the present study,AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa,such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression.In particular,for the diagnosis of clinically significant PCa,the AI methods achieved a higher summary receiver operator characteristic curve(SROC-AUC)than that of the clinical assessment methods(0.87 vs.0.82).For the prediction of adverse pathology,the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods(0.86 vs.0.75).Second,as revealed by the radiomics quality score(RQS),the studies included in the present study presented a relatively high total average RQS of 15.2(11.0–20.0).Further,the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes,but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence,such as prospective studies and open-testing datasets.

Preoperative prediction of perineural invasion of rectal cancer based on a magnetic resonance imaging radiomics model:A dual-center study

BACKGROUND Perineural invasion(PNI)has been used as an important pathological indicator and independent prognostic factor for patients with rectal cancer(RC).Preoperative prediction of PNI status is helpful for individualized treatment of RC.Recently,several radiomics studies have been used to predict the PNI status in RC,demonstrating a good predictive effect,but the results lacked generalizability.The preoperative prediction of PNI status is still challenging and needs further study.AIM To establish and validate an optimal radiomics model for predicting PNI status preoperatively in RC patients.METHODS This retrospective study enrolled 244 postoperative patients with pathologically confirmed RC from two independent centers.The patients underwent preoperative high-resolution magnetic resonance imaging(MRI)between May 2019 and August 2022.Quantitative radiomics features were extracted and selected from oblique axial T2-weighted imaging(T2WI)and contrast-enhanced T1WI(T1CE)sequences.The radiomics signatures were constructed using logistic regression analysis and the predictive potential of various sequences was compared(T2WI,T1CE and T2WI+T1CE fusion sequences).A clinical-radiomics(CR)model was established by combining the radiomics features and clinical risk factors.The internal and external validation groups were used to validate the proposed models.The area under the receiver operating characteristic curve(AUC),DeLong test,net reclassification improvement(NRI),integrated discrimination improvement(IDI),calibration curve,and decision curve analysis(DCA)were used to evaluate the model performance.RESULTS Among the radiomics models,the T2WI+T1CE fusion sequences model showed the best predictive performance,in the training and internal validation groups,the AUCs of the fusion sequence model were 0.839[95%confidence interval(CI):0.757-0.921]and 0.787(95%CI:0.650-0.923),which were higher than those of the T2WI and T1CE sequence models.The CR model constructed by combining clinical risk factors had the best predictive performance.In the training and internal and external validation groups,the AUCs of the CR model were 0.889(95%CI:0.824-0.954),0.889(95%CI:0.803-0.976)and 0.894(95%CI:0.814-0.974).Delong test,NRI,and IDI showed that the CR model had significant differences from other models(P<0.05).Calibration curves demonstrated good agreement,and DCA revealed significant benefits of the CR model.CONCLUSION The CR model based on preoperative MRI radiomics features and clinical risk factors can preoperatively predict the PNI status of RC noninvasively,which facilitates individualized treatment of RC patients.

Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma:A quantitative review with Radiomics Quality Score

BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging(MRI)for different tasks related to the management of patients with hepatocellular carcinoma(HCC).However,its implementation in clinical practice is still far,with many issues related to the methodological quality of radiomic studies.AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score(RQS).METHODS A systematic literature search of PubMed,Google Scholar,and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023.The methodological quality of radiomic studies was assessed using the RQS tool.Spearman’s correlation(ρ)analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies.The level of statistical significance was set at P<0.05.RESULTS One hundred and twenty-seven articles were included,of which 43 focused on HCC prognosis,39 on prediction of pathological findings,16 on prediction of the expression of molecular markers outcomes,18 had a diagnostic purpose,and 11 had multiple purposes.The mean RQS was 8±6.22,and the corresponding percentage was 24.15%±15.25%(ranging from 0.0% to 58.33%).RQS was positively correlated with journal impact factor(IF;ρ=0.36,P=2.98×10^(-5)),5-years IF(ρ=0.33,P=1.56×10^(-4)),number of patients included in the study(ρ=0.51,P<9.37×10^(-10))and number of radiomics features extracted in the study(ρ=0.59,P<4.59×10^(-13)),and time of publication(ρ=-0.23,P<0.0072).CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients,our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.

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.

In vivo imaging reveals a synchronized correlation among neurotransmitter dynamics during propofol and sevoflurane anesthesia

General anesthesia is widely applied in clinical practice.However,the precise mechanism of loss of consciousness induced by general anesthetics remains unknown.Here,we measured the dynamics of five neurotransmitters,includingγ-aminobutyric acid,glutamate,norepinephrine,acetylcholine,and dopamine,in the medial prefrontal cortex and primary visual cortex of C57BL/6 mice through in vivo fiber photometry and genetically encoded neurotransmitter sensors under anesthesia to reveal the mechanism of general anesthesia from a neurotransmitter perspective.Results revealed that the concentrations of γ-aminobutyric acid,glutamate,norepinephrine,and acetylcholine increased in the cortex during propofol-induced loss of consciousness.Dopamine levels did not change following the hypnotic dose of propofol but increased significantly following surgical doses of propofol anesthesia.Notably,the concentrations of the five neurotransmitters generally decreased during sevoflurane-induced loss of consciousness.Furthermore,the neurotransmitter dynamic networks were not synchronized in the non-anesthesia groups but were highly synchronized in the anesthetic groups.These findings suggest that neurotransmitter dynamic network synchronization may cause anesthetic-induced loss of consciousness.