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.

Non-invasive and low-artifact in vivo brain imaging by using a scanning acoustic-photoacoustic dual mode microscopy

Photoacoustic imaging is a potential candidate for in vivo brain imaging,whereas,its imaging performance could be degraded by inhomogeneous multi-layered media,consisted of scalp and skull.In this work,we propose a low-artifact photoacoustic microscopy(LAPAM)scheme,which combines conventional acoustic-resolution photoacoustic microscopy with scanning acoustic microscopy to suppress the reflection artifacts induced by multi-layers.Based on similar propagation characteristics of photoacoustic signals and ultrasonic echoes,the ultrasonic echoes can be employed as the filters to suppress the reflection artifacts to obtain low-artifact photoacoustic images.Phantom experiment is used to validate the effectiveness of this method.Furthermore,LAPAM is applied for in-vivo imaging mouse brain without removing the scalp and the skull.Experimental results show that the proposed method successfully achieves the low-artifact brain image,which demonstrates the practical applicability of LAPAM.This work might improve the photoacoustic imaging quality in many biomedical applications which involve tissues with complex acoustic properties,such as brain imaging through scalp and skull.

Functional brain imaging studies on specificity of meridian and acupoints

At present, the specificity of meridians and acupoints has been studied using functional brain imaging techniques from many standpoints, including meridians, acupoints, and sham acupoints, as well as different meridians and acupoints, coordination of acupoints, and factors influencing meridian and acupoint specificity Preliminary experimental data have demonstrated that acupuncture at meridians and acupoints is specific with regard to brain neural information. However, research findings are contradictory, which may be related to brain functional complexity, resolution of functional brain imaging techniques, and experimental design. Future studies should further improve study method, and should strictly control experimental conditions to better analyze experimental data and acquire more beneficial data. Because of its many advantages, the functional brain imaging technique is a promising method for studying meridian and acupoint specificity.

inMTSCCA:An Integrated Multi-task Sparse Canonical Correlation Analysis for Multi-omic Brain Imaging Genetics

Identifying genetic risk factors for Alzheimer's disease(AD)is an important research topic.To date,different endophenotypes,such as imaging-derived endophenotypes and proteomic expression-derived endophenotypes,have shown the great value in uncovering risk genes compared to case-control studies.Biologically,a co-varying pattern of different omics-derived endophenotypes could result from the shared genetic basis.However,existing methods mainly focus on the effect of endophenotypes alone;the effect of cross-endophenotype(CEP)associations remains largely unexploited.In this study,we used both endophenotypes and their CEP associations of multi-omic data to identify genetic risk factors,and proposed two integrated multi-task sparse canonical correlation analysis(inMTSCCA)methods,i.e.,pairwise endophenotype correlationguided MTSCCA(pcMTSCCA)and high-order endophenotype correlation-guided MTSCCA(hocMTSCCA).pcMTSCCA employed pairwise correlations between magnetic resonance imaging(MRI)-derived,plasma-derived,and cerebrospinal fluid(CSF)-derived endophenotypes as an additional penalty.hocMTSCCA used high-order correlations among these multi-omic data for regularization.To figure out genetic risk factors at individual and group levels,as well as altered endophenotypic markers,we introduced sparsity-inducing penalties for both models.We compared pcMTSCCA and hocMTSCCA with three related methods on both simulation and real(consisting of neuroimaging data,proteomic analytes,and genetic data)datasets.The results showed that our methods obtained better or comparable canonical correlation coefficients(CCCs)and better feature subsets than benchmarks.Most importantly,the identified genetic loci and heterogeneous endophenotypic markers showed high relevance.Therefore,jointly using multi-omic endophenotypes and their CEP associations is promising to reveal genetic risk factors.

Machine Learning for Brain Imaging Genomics Methods:A Review

In the past decade,multimodal neuroimaging and genomic techniques have been increasingly developed.As an interdiscip-linary topic,brain imaging genomics is devoted to evaluating and characterizing genetic variants in individuals that influence phenotyp-ic measures derived from structural and functional brain imaging.This technique is capable of revealing the complex mechanisms by macroscopic intermediates from the genetic level to cognition and psychiatric disorders in humans.It is well known that machine learn-ing is a powerful tool in the data-driven association studies,which can fully utilize priori knowledge(intercorrelated structure informa-tion among imaging and genetic data)for association modelling.In addition,the association study is able to find the association between risk genes and brain structure or function so that a better mechanistic understanding of behaviors or disordered brain functions is ex-plored.In this paper,the related background and fundamental work in imaging genomics are first reviewed.Then,we show the univari-ate learning approaches for association analysis,summarize the main idea and modelling in genetic-imaging association studies based on multivariate machine learning,and present methods for joint association analysis and outcome prediction.Finally,this paper discusses some prospects for future work.

Bibliometric study and critical individual literature review of driving behavior analysis methods based on brain imaging from 1993 to 2022

Brain imaging methods have effectively revealed drivers’underlying psychological and neural processes when they perform driving tasks and promote driving behavior research in a more scientific direction.With research no longer limited to indirect inferences about external behavior,some researchers combine behavior and driver brain activity to understand the human factors in driving essentially.However,most researchers in the field of driving behavior still have little understanding of how brain imaging methods are used.This paper aims to review and analyze the application of brain imaging methods in driving behavior research,including bibliometric analysis and an individual critical literature review.Regarding bibliometric analysis,this field’s knowledge structure and development trend are described macroscopically,using data such as annual distribution of publications,country/region statistics and partnerships,publication sources,literature co-citation analysis,and keyword co-occurrence analysis.In a review of the individual critical literature,eight research themes were identified that examined driving behavior using brain imaging methods:substance consumption,fatigue or sleep deprivation,workload,distraction,aging brains,brain impairment and other diseases,automated/semi-automated environments,emotions influence and risk-taking,and general driving process.In addition,the study reports on six brain imaging methods and their advantages and disadvantages,involving electroencephalography(EEG),functional magnetic resonance imaging(fMRI),functional near-infrared spectroscopy(fNIRS),magnetoencephalography(MEG),positron emission tomography(PET),and transcranial magnetic stimulation(TMS).The contribution of this study is twofold.The first part relates to providing the researchers with a comprehensive understanding of the field’s knowledge structure and development trends.The second part goes beyond reviewing and analyzing previous studies,and the discussion section points out the directions and challenges for future research.

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.

Optical Brain Imaging： A Powerful Tool for Neuroscience

As the control center of organisms, the brain remains little understood due to its complexity. Taking advantage of imaging methods, scientists have found an accessible approach to unraveling the mystery of neuroscience. Among these methods, optical imaging techniques are widely used due to their high molecular specificity and single-molecule sensitivity. Here, we overview several optical imaging techniques in neuroscience of recent years, including brain clearing, the micro-optical sectioning tomography system, and deep tissue imaging.

Three-dimensional,isotropic imaging of mouse brain using multi-view deconvolution light sheet microscopy

We present a threedimensional(3D)isotropic imaging of mouse brain using light-sheet fuo-rescent microscopy(LSFM)in conjumction with a multi-view imaging computation.Unlike common single view LSFM is used for mouse brain imaging,the brain tissue is 3D imaged under eight views in our study,by a home-built selective plane ilumination microscopy(SPIM).An output image containing complete structural infornation as well as significantly improved res olution(~4 times)are then computed based on these eight views of data,using a bead-guided multi-view registration and deconvolution.With superior imaging quality,the astrocyte and pyrarmidal neurons together with their subcellular nerve fbers can be clearly visualized and segmented.With further incuding other computational methods,this study can be potentially scaled up to map the conectome of whole mouse brain with a simple light.sheet microscope.

Nanoparticles as contrast agents for photoacoustic brain imaging

Photoacoustic(PA)imaging has emerged as a promising technique for real-time detection and diagnosis of brain-related pathologies,due to its advantages in deep penetration of ultrasound imaging and high resolution of optical fluorescence imaging.We herein provide an overview on the latest developments of nanoparticles as contrast agents specifically designed for PA imaging of brain tumor,and brain vascular and other brain-related diseases.Five design considerations of high-performance PA contrast agents for brain-related disease diagnosis are discussed,which include(1)strong absorption in NIR or NIR-Ⅱ window,(2)good biocompatibility,(3)high photothermal conversion efficiency,(4)precise nanostructure control,and(5)spe-cific targeting capability.Challenges and perspectives of developing more robust and universal contrast agents for enhanced PA imaging are discussed at the end.

Brain Imaging Could Spot Liars

美国研究人员利用大脑功能性磁共振成像技术(fMRI)发现,人在说谎和讲真话时,其大脑的活动区域是不一样的;与讲实话相比,说谎时人的大脑可能要付出更多的劳动,这不是人的意识可以控制的。这一发现有望帮助专家开发新的测谎技术。

Susceptibility-weighted imaging is suitable for evaluating signal strength in different brain regions of a rabbit model of acute hemorrhagic anemia

Acute hemorrhagic anemia can decrease blood flow and oxygen supply to brain, and affect its physiological function. While detecting changes in brain function in patients with acute hemorrhagic anemia is helpful for preventing neurological complications and evaluating therapeutic effects, clinical changes in the nervous systems of these patients have not received much attention. In part, this is because current techniques can only indirectly detect changes in brain function following onset of anemia, which leads to lags between real changes in brain function and their detection.

Free form deformation and symmetry constraint‐based multimodal brain image registration using generative adversarial nets

Multi‐modal brain image registration has been widely applied to functional localisation,neurosurgery and computational anatomy.The existing registration methods based on the dense deformation fields involve too many parameters,which is not conducive to the exploration of correct spatial correspondence between the float and reference images.Meanwhile,the unidirectional registration may involve the deformation folding,which will result in the change of topology during registration.To address these issues,this work has presented an unsupervised image registration method using the free form deformation(FFD)and the symmetry constraint‐based generative adversarial networks(FSGAN).The FSGAN utilises the principle component analysis network‐based structural representations of the reference and float images as the inputs and uses the generator to learn the FFD model parameters,thereby producing two deformation fields.Meanwhile,the FSGAN uses two discriminators to decide whether the bilateral registration have been realised simultaneously.Besides,the symmetry constraint is utilised to construct the loss function,thereby avoiding the deformation folding.Experiments on BrainWeb,high grade gliomas,IXI and LPBA40 show that compared with state‐of‐the‐art methods,the FSGAN provides superior performance in terms of visual comparisons and such quantitative indexes as dice value,target registration error and computational efficiency.

Multi Class Brain Cancer Prediction System Empowered with BRISK Descriptor

Magnetic Resonance Imaging(MRI)is one of the important resources for identifying abnormalities in the human brain.This work proposes an effective Multi-Class Classification(MCC)system using Binary Robust Invariant Scalable Keypoints(BRISK)as texture descriptors for effective classification.Atfirst,the potential Region Of Interests(ROIs)are detected using features from the acceler-ated segment test algorithm.Then,non-maxima suppression is employed in scale space based on the information in the ROIs.The discriminating power of BRISK is examined using three machine learning classifiers such as k-Nearest Neighbour(kNN),Support Vector Machine(SVM)and Random Forest(RF).An MCC sys-tem is developed which classifies the MRI images into normal,glioma,meningio-ma and pituitary.A total of 3264 MRI brain images are employed in this study to evaluate the proposed MCC system.Results show that the average accuracy of the proposed MCC-RF based system is 99.62%with a sensitivity of 99.16%and spe-cificity of 99.75%.The average accuracy of the MCC-kNN system is 93.65%and 97.59%by the MCC-SVM based system.

De-Noising Brain MRI Images by Mixing Concatenation and Residual Learning(MCR)

Brain magnetic resonance images(MRI)are used to diagnose the different diseases of the brain,such as swelling and tumor detection.The quality of the brain MR images is degraded by different noises,usually salt&pepper and Gaussian noises,which are added to the MR images during the acquisition process.In the presence of these noises,medical experts are facing problems in diagnosing diseases from noisy brain MR images.Therefore,we have proposed a de-noising method by mixing concatenation,and residual deep learning techniques called the MCR de-noising method.Our proposed MCR method is to eliminate salt&pepper and gaussian noises as much as possible from the brain MRI images.The MCR method has been trained and tested on the noise quantity levels 2%to 20%for both salt&pepper and gaussian noise.The experiments have been done on publically available brain MRI image datasets,which can easily be accessible in the experiments and result section.The Structure Similarity Index Measure(SSIM)and Peak Signal-to-Noise Ratio(PSNR)calculate the similarity score between the denoised images by the proposed MCR method and the original clean images.Also,the Mean Squared Error(MSE)measures the error or difference between generated denoised and the original images.The proposed MCR denoising method has a 0.9763 SSIM score,84.3182 PSNR,and 0.0004 MSE for salt&pepper noise;similarly,0.7402 SSIM score,72.7601 PSNR,and 0.0041 MSE for Gaussian noise at the highest level of 20%noise.In the end,we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters.

An Improved Soft Subspace Clustering Algorithm for Brain MR Image Segmentation

In recent years,the soft subspace clustering algorithm has shown good results for high-dimensional data,which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features.The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information,which has strong results for image segmentation,but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center.However,the clustering algorithmis susceptible to the influence of noisydata and reliance on initializedclustering centers andfalls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects.To address these problems,a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed,which combines the generalized noise technique,relaxes the equational weight constraint in the objective function as the boundary constraint,and uses a genetic algorithm as a method to optimize the initialized clustering center.The genetic algorithm finds the best clustering center and reduces the algorithm’s dependence on the initial clustering center.The experiment verifies the robustness of the algorithm,as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People’s Hospital with specific high accuracy for clinical medicine.

Functional magnetic resonance imaging evidence for activated functional brain areas following acupoint needling in the extremities

Totally three articles focusing on functional magnetic resonance imaging features of brain function in the activated brain regions of stroke patients undergoing acupuncture on the healthy limbs and healthy controls undergoing acupuncture on the lower extremities are published in three issues. We hope that our readers find these papers useful to their research.

Neural stem cell-derived exosomes promote mitochondrial biogenesis and restore abnormal protein distribution in a mouse model of Alzheimer's disease

Mitochondrial dysfunction is a hallmark of Alzheimer’s disease.We previously showed that neural stem cell-derived extracellular vesicles improved mitochondrial function in the cortex of AP P/PS1 mice.Because Alzheimer’s disease affects the entire brain,further research is needed to elucidate alterations in mitochondrial metabolism in the brain as a whole.Here,we investigated the expression of several important mitochondrial biogenesis-related cytokines in multiple brain regions after treatment with neural stem cell-derived exosomes and used a combination of whole brain clearing,immunostaining,and lightsheet imaging to clarify their spatial distribution.Additionally,to clarify whether the sirtuin 1(SIRT1)-related pathway plays a regulatory role in neural stem cell-de rived exosomes interfering with mitochondrial functional changes,we generated a novel nervous system-SIRT1 conditional knoc kout AP P/PS1mouse model.Our findings demonstrate that neural stem cell-de rived exosomes significantly increase SIRT1 levels,enhance the production of mitochondrial biogenesis-related fa ctors,and inhibit astrocyte activation,but do not suppress amyloid-βproduction.Thus,neural stem cell-derived exosomes may be a useful therapeutic strategy for Alzheimer’s disease that activates the SIRT1-PGC1αsignaling pathway and increases NRF1 and COXIV synthesis to improve mitochondrial biogenesis.In addition,we showed that the spatial distribution of mitochondrial biogenesis-related factors is disrupted in Alzheimer’s disease,and that neural stem cell-derived exosome treatment can reverse this effect,indicating that neural stem cell-derived exosomes promote mitochondrial biogenesis.

Brain MRI Findings in Infantile Spasm: Outcome Correlations in a Patient Cohort

Background: Infantile spasm is a type of pediatric seizure often associated with a negative prognosis. The aim of this study was to evaluate the role of Magnetic Resonance Imaging (MRI) in categorization and neurodevelopmental outcomes in children with infantile spasm. Materials and Methods: A retrospective study of the clinical charts and MRI findings of infants diagnosed with infantile spasm between December 2007 and February 2014. Results: A total of 26 children (16 males;1.6/1) were included: 8 of unknown etiology and 18 with a genetic/structural-metabolic causes. Unknown etiology cases revealed normal brain MRI in 5/8 (62.5%). In the genetic/ structural-metabolic group, only 2/18 (11.1%) had normal imaging. Abnormal imaging findings significantly correlated with genetic/structural-metabolic infantile spasm which had unfavorable neurodevelopmental outcome. Conclusion: Neuroimaging conveys substantial information to the further categorization of children with infantile spasm, providing not only relevant information of the underlying cause but also the prediction of the neurodevelopmental outcome.

An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations

Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the brain.Recently,several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation;however,the algorithms become trapped in local minima and have low convergence speeds,particularly as the number of threshold levels increases.Consequently,in this paper,we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm(JSA)(an optimizer).We modify the JSA to prevent descents into local minima,and we accelerate convergence toward optimal solutions.The improvement is achieved by applying two novel strategies:Rankingbased updating and an adaptive method.Ranking-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed solutions.We develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution;we allow a small amount of exploration to avoid descents into local minima.The two strategies are integrated with the JSA to produce an improved JSA(IJSA)that optimally thresholds brain MR images.To compare the performances of the IJSA and JSA,seven brain MR images were segmented at threshold levels of 3,4,5,6,7,8,10,15,20,25,and 30.IJSA was compared with several other recent image segmentation algorithms,including the improved and standard marine predator algorithms,the modified salp and standard salp swarm algorithms,the equilibrium optimizer,and the standard JSA in terms of fitness,the Structured Similarity Index Metric(SSIM),the peak signal-to-noise ratio(PSNR),the standard deviation(SD),and the Features Similarity Index Metric(FSIM).The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM,the PSNR,the objective values,and the SD;in terms of the SSIM,IJSA was competitive with the others.