The pig as a preclinical traumatic brain injury model:current models,functional outcome measures,and translational detection strategies

Traumatic brain injury(TBI) is a major contributor of long-term disability and a leading cause of death worldwide. A series of secondary injury cascades can contribute to cell death, tissue loss, and ultimately to the development of functional impairments. However, there are currently no effective therapeutic interventions that improve brain outcomes following TBI. As a result, a number of experimental TBI models have been developed to recapitulate TBI injury mechanisms and to test the efficacy of potential therapeutics. The pig model has recently come to the forefront as the pig brain is closer in size, structure, and composition to the human brain compared to traditional rodent models, making it an ideal large animal model to study TBI pathophysiology and functional outcomes. This review will focus on the shared characteristics between humans and pigs that make them ideal for modeling TBI and will review the three most common pig TBI models–the diffuse axonal injury, the controlled cortical impact, and the fluid percussion models. It will also review current advances in functional outcome assessment measures and other non-invasive, translational TBI detection and measurement tools like biomarker analysis and magnetic resonance imaging. The use of pigs as TBI models and the continued development and improvement of translational assessment modalities have made significant contributions to unraveling the complex cascade of TBI sequela and provide an important means to study potential clinically relevant therapeutic interventions.

Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model

Traumatic brain injury(TBI) at a young age can lead to the development of long-term functional impairments. Severity of injury is well demonstrated to have a strong influence on the extent of functional impairments;however, identification of specific magnetic resonance imaging(MRI) biomarkers that are most reflective of injury severity and functional prognosis remain elusive. Therefore, the objective of this study was to utilize advanced statistical approaches to identify clinically relevant MRI biomarkers and predict functional outcomes using MRI metrics in a translational large animal piglet TBI model. TBI was induced via controlled cortical impact and multiparametric MRI was performed at 24 hours and 12 weeks post-TBI using T1-weighted, T2-weighted, T2-weighted fluid attenuated inversion recovery, diffusion-weighted imaging, and diffusion tensor imaging. Changes in spatiotemporal gait parameters were also assessed using an automated gait mat at 24 hours and 12 weeks post-TBI. Principal component analysis was performed to determine the MRI metrics and spatiotemporal gait parameters that explain the largest sources of variation within the datasets. We found that linear combinations of lesion size and midline shift acquired using T2-weighted imaging explained most of the variability of the data at both 24 hours and 12 weeks post-TBI. In addition, linear combinations of velocity, cadence, and stride length were found to explain most of the gait data variability at 24 hours and 12 weeks post-TBI. Linear regression analysis was performed to determine if MRI metrics are predictive of changes in gait. We found that both lesion size and midline shift are significantly correlated with decreases in stride and step length. These results from this study provide an important first step at identifying relevant MRI and functional biomarkers that are predictive of functional outcomes in a clinically relevant piglet TBI model. This study was approved by the University of Georgia Institutional Animal Care and Use Committee(AUP: A2015 11-001) on December 22, 2015.

Effects of exercise on brain functions in diabetic animal models

Human life span has dramatically increased over several decades,and the quality of life has been considered to be equally important.However,diabetes mellitus(DM) characterized by problems related to insulin secretion and recognition has become a serious health problem in recent years that threatens human health by causing decline in brain functions and finally leading to neurodegenerative diseases.Exercise is recognized as an effective therapy for DM without medication administration.Exercise studiesusing experimental animals are a suitable option to overcome this drawback,and animal studies have improved continuously according to the needs of the experimenters.Since brain health is the most significant factor in human life,it is very important to assess brain functions according to the different exercise conditions using experimental animal models.Generally,there are two types of DM; insulin-dependent type 1 DM and an insulin-independent type 2 DM(T2DM); however,the author will mostly discuss brain functions in T2 DM animal models in this review.Additionally,many physiopathologic alterations are caused in the brain by DM such as increased adiposity,inflammation,hormonal dysregulation,uncontrolled hyperphagia,insulin and leptin resistance,and dysregulation of neurotransmitters and declined neurogenesis in the hippocampus and we describe how exercise corrects these alterations in animal models.The results of changes in the brain environment differ according to voluntary,involuntary running exercises and resistance exercise,and gender in the animal studies.These factors have been mentioned in this review,and this review will be a good reference for studying how exercise can be used with therapy for treating DM.

How does the motor relearning program improve neurological function of brain ischemia monkeys?

The motor relearning program can significantly improve various functional disturbance induced by ischemic cerebrovascular diseases. However, its mechanism of action remains poorly understood. In injured brain tissues, glial fibrillary acidic protein and neurofilament protein changes can reflect the condition of injured neurons and astrocytes, while vascular endothelial growth factor and basic fibroblast growth factor changes can indicate angiogenesis. In the present study, we induced ischemic brain injury in the rhesus macaque by electrocoagulation of the M1 segment of the right middle cerebral artery. The motor relearning program was conducted for 60 days from the third day after model establishment. Immunohistochemistry and single-photon emission CT showed that the numbers of glial fibrillary acidic protein-, neurofilament protein-, vascular endothelial growth factor- and basic fibroblast growth factor-positive cells were significantly increased in the infarcted side compared with the contralateral hemisphere following the motor relearning program. Moreover, cerebral blood flow in the infarcted side was significantly improved. The clinical rating scale for stroke was used to assess neurological function changes in the rhesus macaque following the motor relearning program. Results showed that motor function was improved, and problems with consciousness, self-care ability and balance function were significantly ameliorated. These findings indicate that the motor relearning program significantly promoted neuronal regeneration, repair and angiogenesis in the surroundings of the infarcted hemisphere, and improve neurological function in the rhesus macaque following brain ischemia.

Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models

Brain encoding and decoding via functional magnetic resonance imaging(fMRI)are two important aspects of visual perception neuroscience.Although previous researchers have made significant advances in brain encoding and decoding models,existing methods still require improvement using advanced machine learning techniques.For example,traditional methods usually build the encoding and decoding models separately,and are prone to overfitting on a small dataset.In fact,effectively unifying the encoding and decoding procedures may allow for more accurate predictions.In this paper,we first review the existing encoding and decoding methods and discuss the potential advantages of a“bidirectional”modeling strategy.Next,we show that there are correspondences between deep neural networks and human visual streams in terms of the architecture and computational rules.Furthermore,deep generative models(e.g.,variational autoencoders(VAEs)and generative adversarial networks(GANs))have produced promising results in studies on brain encoding and decoding.Finally,we propose that the dual learning method,which was originally designed for machine translation tasks,could help to improve the performance of encoding and decoding models by leveraging large-scale unpaired data.

Functional magnetic resonance imaging and the brain: A brief review

Functional magnetic resonance imaging(fMRI) is em-ployed in many behavior analysis studies, with blood oxygen level dependent-(BOLD-) contrast imaging being the main method used to generate images. The use of BOLD-contrast imaging in f MRI has been refined over the years, for example, the inclusion of a spin echo pulse and increased magnetic strength were shown to produce better recorded images. Taking careful precautions to control variables during measurement, comparisons between different specimen groups can be illustrated by f MRI imaging using both quantitative and qualitative methods. Differences have been observed in comparisons of active and resting, developing and aging, and defective and damaged brains in various studies. However, cognitive studies using f MRI still face a number of challenges in interpretation that can only be overcome by imaging large numbers of samples. Furthermore, f MRI studies of brain cancer, lesions and other brain pathologies of both humans and animals are still to be explored.

Studies in Brain Functional Networks Based on Complex Networks

The purpose of the paper is to provide a way to model the brain functional network based on the complex networks with brain anatomical architecture. We introduce the brain structural and functional researches, and delineate the brain anatomical and functional networks based on complex networks, then we discuss the brain functional complex network models; at last we put forward the brain functional networks modeling process and the data processing with fMRI （functional magnetic resonance imaging） in detailed.

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.

Microglia and astrocytes mediate synapse engulfment in a MER tyrosine kinase-dependent manner after traumatic brain injury

Recent studies have shown that microglia/macrophages and astrocytes can mediate synaptic phagocytosis through the MER proto-oncokinase in developmental or stroke models,but it is unclear whether the same mechanism is also active in traumatic brain injury.In this study,we established a mouse model of traumatic brain injury and found that both microglia/macrophages and astrocytes phagocytosed synapses and expression of the MER proto-oncokinase increased 14 days after injury.Specific knockout of MER in microglia/macrophages or astrocytes markedly reduced injury volume and greatly improved neurobehavioral function.In addition,in both microglia/macrophages-specific and astrocytes-specific MER knock-out mice,the number of microglia/macrophage and astrocyte phagocytosing synapses was markedly decreased,and the total number of dendritic spines was increased.Our study suggested that MER proto-oncokinase expression in microglia/macrophages and astrocytes may play an important role in synaptic phagocytosis,and inhibiting this process could be a new strategy for treating traumatic brain injury.

Potential rat model of anxiety-like gastric hypersensitivity induced by sequential stress

AIM To establish a rat model of anxiety-like gastric hyper-sensitivity(GHS) of functional dyspepsia(FD) induced by novel sequential stress.METHODS Animal pups were divided into two groups from postnatal day 2: controls and the sequential-stress-treated. The sequential-stress-treated group received maternal separation and acute gastric irritation early in life and restraint stress in adulthood; controls were reared undisturbed with their mothers. Rats in both groups were followed to adulthood(8 wk) at which point the anxietylike behaviors and visceromotor responses to gastric distention(20-100 mm Hg) and gastric emptying were tested. Meanwhile, alterations in several anxiety-related brain-stomach modulators including 5-hydroxytryptamine(5-HT), γ-aminobutyric acid(GABA), brain-derived neurotrophic factor(BDNF) and nesfatin-1 in the rat hippocampus, plasma and gastric fundus and the 5-HT1 A receptor(5-HT1 AR) in the hippocampal CA1 subfield and the mucosa of the gastric fundus were examined.RESULTS Sequential-stress-treated rats simultaneously demonstrated anxiety-like behaviors and GHS in dose-dependent manner compared with the control group. Although rats in both groups consumed similar amount of solid food, the rate of gastric emptying was lower in the sequentialstress-treated rats than in the control group. Sequential stress significantly decreased the levels of 5-HT(51.91 ± 1.88 vs 104.21 ± 2.88, P < 0.01), GABA(2.38 ± 0.16 vs 5.01 ± 0.13, P < 0.01) and BDNF(304.40 ± 10.16 vs 698.17 ± 27.91, P < 0.01) in the hippocampus but increased the content of nesfatin-1(1961.38 ± 56.89 vs 1007.50 ± 33.05, P < 0.01) in the same site; significantly decreased the levels of 5-HT(47.82 ± 2.29 vs 89.45 ± 2.61, P < 0.01) and BDNF(257.05 ± 12.89 vs 536.71 ± 20.73, P < 0.01) in the plasma but increased the content of nesfatin-1 in it(1391.75 ± 42.77 vs 737.88 ± 33.15, P < 0.01); significantly decreased the levels of 5-HT(41.15 ± 1.81 vs 89.17 ± 2.31, P < 0.01) and BDNF(226.49 ± 12.10 vs 551.36 ± 16.47, P < 0.01) in the gastric fundus but increased the content of nesfatin-1 in the same site(1534.75 ± 38.52 vs 819.63 ± 38.04, P < 0.01). The expressions of 5-HT1 AR in the hippocampal CA1 subfield and the mucosa of the gastric fundus were down-regulated measured by IHC(Optical Density value: Hippocampus 15253.50 ± 760.35 vs 21149.75 ± 834.13; gastric fundus 15865.25 ± 521.24 vs 23865.75 ± 1868.60; P < 0.05, respectively) and WB(0.38 ± 0.01 vs 0.57 ± 0.03, P < 0.01)(n = 8 in each group). CONCLUSION Sequential stress could induce a potential rat model of anxiety-like GHS of FD, which could be used to research the mechanisms of this intractable disease.

基于多模态影像学的轻度创伤性脑损伤脑功能评估的研究进展

轻度创伤性脑损伤(mild traumatic brain injury,mTBI)具有病理生理复杂、早期诊断难、伴有不同程度脑功能障碍等特点,需早期干预才能有效恢复如注意力不集中、记忆力减退等脑功能障碍。上述功能障碍与大脑默认网络、海马、基底节等脑区功能异常密切关联,而影像学可以准确量化评估这些脑功能区的改变。高场强磁共振成像技术的出现,使得影像学成为mTBI临床早期诊断与临床治疗评估的关键技术。基于mTBI动物模型及临床患者,本文从磁共振成像中反映工作记忆的动脉自旋回波、反映认知缺陷的弥散张量成像、反映淋巴转运异常的超高场强磁共振增强成像以及反映颅脑冲击伤损伤程度的正电子发射计算机断层扫描术与脑电图等方面作一综述,旨在为mTBI军事医学研究提供参考。

基于优化零模型的脑功能超网络拓扑结构分析及多特征融合分类

脑功能超网络已广泛用于脑疾病分类,但大多研究集中于改进静态超网络模型的构建来提高分类准确率,主要使用聚类系数等常见节点拓扑属性量化脑功能超网络,忽略了超网络拓扑属性所包含的有效信息。然而研究表明超边所体现的高阶模式可以更好地表征脑网络整体结构,超边间的交互则可以反映超边间的空间传递信息能力,有助于更好地理解脑疾病的神经机制。考虑到该问题,提出多组超边属性,并结合已有节点属性综合表征脑功能超网络拓扑信息。然而,由于超网络超边特性,导致拓扑信息计算代价较为昂贵,并且多个拓扑属性之间可能存在冗余性较高的问题。因此,进一步优化已有零模型使其适配脑功能超网络,并基于优化的零模型评估拓扑属性间的依赖关系,最终融合依赖性较小的拓扑属性参与分类模型的构建。结果表明,拓扑属性间的依赖关系有所不同,含有超边的拓扑属性与其余拓扑属性的依赖性较小。此外,融合依赖性较小的多拓扑属性特征的分类性能优于传统方法,达到89.39%。这表明提取脑功能超网络的拓扑信息时,不仅需要考虑节点拓扑属性,还需考虑超边信息。同时在多组拓扑属性表征脑功能超网络拓扑信息基础上,选取依赖性不强即冗余性较低的拓扑属性能够提高组间差异表征能力,最终提高脑疾病分类性能。

Functional Connectivity-Based Modelling Simulates SubjectSpecific Network Spreading Effects of Focal Brain Stimulation

Neurostimulation remarkably alleviates the symptoms in a variety of brain disorders by modulating the brain-wide network. However, how brain-wide effects on the direct and indirect pathways evoked by focal neurostimulation elicit therapeutic effects in an individual patient is unknown. Understanding this remains crucial for advancing neural circuit-based guidance to optimize candidate patient screening, pre-surgical target selection, and post-surgical parameter tuning. To address this issue, we propose a functional brain connectome-based modeling approach that simulates the spreading effects of stimulating different brain regions and quantifies the rectification of abnormal network topology in silico. We validated these analyses by pinpointing nuclei in the basal ganglia circuits as top-ranked targets for 43 local patients with Parkinson’s disease and 90 patients from a public database. Individual connectome-based analysis demonstrated that the globus pallidus was the best choice for 21.1% and the subthalamic nucleus for 19.5% of patients. Down-regulation of functional connectivity(up to 12%) at these prioritized targets optimally maximized the therapeutic effects. Notably, the priority rank of the subthalamic nucleus significantly correlated with motor symptom severity(Unified Parkinson’s Disease Rating Scale III) in the local cohort. These findings underscore the potential of neural network modeling for advancing personalized brain stimulation therapy,and warrant future experimental investigation to validate its clinical utility.

基于YOLOv8改进的脑癌检测算法

自动检测脑部肿瘤在磁共振成像中的位置是一个复杂、繁重的任务,需要耗费大量时间和资源。传统识别方案经常出现误解、遗漏和误导的情况,从而影响患者的治疗进度,对患者的生命安全产生影响。为了进一步提高鉴定的效果,引入了4项关键改进措施。首先,采用了高效的多尺度注意力EMA(Efficient Multi-scale Attention),这种方法既可以对全局信息进行编码,也可以对信息进行重新校准,同时通过并行的分支输出特征进行跨维度的交互,使信息进一步聚合。其次,引入了BiFPN(Bidirectional Feature Pyramid Network)模块,并对其结构进行改进,以便缩短每一次检测所需要的时间,同时提升图像识别效果。然后采用MDPIoU损失函数和Mish激活函数进行改进,进一步提高检测的准确度。最后进行仿真实验,实验结果表明,改进的YOLOv8算法在脑癌检测中的精确率、召回率、平均精度均值均有提升,其中Precision提高了4.48%,Recall提高了2.64%,mAP@0.5提高了2.6%,mAP@0.5:0.9提高了7.0%。

基于fNIRS和神经反馈的记忆认知训练研究

该研究使用功能性近红外光谱技术和神经反馈机制开发了记忆任务训练系统,并对被试的脑电信号进行采集和分析。结果显示,参与神经反馈任务的被试在前额叶部右半脑区具有更高的相关性脑网络节点度和平均集群系数,中介中心度的分散程度相对降低。此外,被试的前额叶部左半脑区在神经反馈任务中的核心地位上升。通过通道网络模型和支持向量机模型对脑数据进行分类,结果表明两个模型在任务态和静息态的分类准确率高于反馈任务和对照任务,且通道网络模型的分类准确率更高。该研究结果表明神经反馈任务的被试具有明显的脑数据特征,并且这些特征能够被有效识别。

基于eCASH模式的综合性干预联合早期四级康复训练在ICU重症脑外伤患者康复中的应用价值分析

目的探讨基于eCASH模式的综合性干预联合早期四级康复训练在重症监护病房(ICU)重症脑外伤患者康复中的应用价值。方法94例ICU重症脑外伤患者,通过抛硬币法分为常规组和联合组,各47例。常规组给予常规护理方式干预,联合组在常规组基础上加用基于eCASH模式的综合性干预联合早期四级康复训练。比较两组的临床指标、并发症发生情况及干预前后的神经功能、意识状态、生活质量。结果联合组的机械通气时间(7.24±1.31)d、拔管时间(8.07±1.48)d、ICU时间(10.39±1.98)d、住院时间(13.36±2.03)d短于常规组的(9.86±1.80)、(10.18±1.96)、(13.38±2.05)、(16.52±2.67)d(P<0.05)。干预后,两组意识水平、面瘫和感觉、凝视和视野、上下肢活动、语言和构音障碍评分均较本组干预前显著降低,且联合组意识水平评分(1.54±0.37)分、面瘫和感觉评分(1.36±0.32)分、凝视和视野评分(0.92±0.24)分、上下肢活动评分(3.12±0.80)分、语言和构音障碍评分(0.95±0.26)分均低于常规组的(2.38±0.61)、(2.07±0.57)、(1.68±0.45)、(6.54±1.28)、(1.72±0.47)分(P<0.05)。干预后,两组睁眼、语言反应、肢体运动评分均较本组干预前显著提升,且联合组睁眼评分(3.54±0.46)分、语言反应评分(4.37±0.60)分、肢体运动评分(5.13±0.81)分高于常规组的(3.03±0.42)、(3.86±0.57)、(4.38±0.69)分(P<0.05)。干预后,两组运动、认知评分均较本组干预前显著提升,且联合组运动评分(67.52±8.36)分、认知评分(28.59±5.66)分高于常规组的(60.63±7.59)、(23.65±4.92)分(P<0.05)。与常规组的19.15%比较,联合组并发症发生率4.26%更低(P<0.05)。结论在ICU重症脑外伤患者康复中应用基于eCASH模式的综合性干预联合早期四级康复训练,能够明显改善患者临床指标、神经功能、意识状态、生活质量,降低并发症发生率。

强迫症的功能脑网络机制研究进展

强迫症(obsessive-compulsive disorder,OCD)是一种以强迫思维、强迫行为为主要临床表现的常见精神疾病。近年来,许多基于功能磁共振成像的影像学研究关注到强迫症状与大尺度脑网络之间的关联,现就OCD病理生理过程中涉及的默认网络、突显网络及额顶网络的功能异常,结合三网络模型假说,对强迫症状所对应的脑网络机制研究现状进行综述,以期为OCD的早期识别及干预提供新线索。

Research on Modeling Approach of Brain Function Network Based on Anatomical Distance

The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function network. Three models based on anatomical distance, the number of common neighbor, or anatomical distance and the number of common neighbor are designed. Basing on residuals creates the evaluation criteria for selecting the optimal brain function model network in each class model. The model is selected to simulate the human real brain function network by comparison with real data functional magnetic resonance imaging(f MRI)network. Finally, the result shows that the best model only is based on anatomical distance.

电针不同穴位对功能性腹泻大鼠血清和组织中P物质、血管活性肠肽的影响

【目的】观察不同穴位处方的电针疗法对功能性腹泻(FD)大鼠血清和组织中P物质(SP)、血管活性肠肽(VIP)含量的影响。【方法】将SD大鼠50只随机分为5组,即正常组、模型组、电针Ⅰ组(取双侧天枢、大肠俞)、电针Ⅱ组(取双侧曲池、上巨虚)、电针Ⅲ组(取单侧天枢、大肠俞、曲池、上巨虚),采用番泻叶灌胃法建立FD大鼠模型,电针各组分别按设定穴位给予电针治疗,连续治疗10 d后检测各组血清和组织中SP、VIP含量的变化。【结果】除电针Ⅱ组对大鼠血清VIP含量无显著影响外,各电针组均可使血清和组织中SP含量显著降低,VIP含量显著升高,与模型组比较差异有统计学意义(P<0.05或P<0.01)。【结论】电针可能通过降低大鼠血清和组织中SP含量、提高VIP含量而改善功能性腹泻,电针Ⅰ组作用优于其他2组。

对语言神经机制的新认识

本文针对语言神经机制的研究现状 ,分析了失语症和裂脑人研究的成果 ,并且较为详细地介绍了最近十年脑功能成像得到的有关语言神经活动的新成就 ,在此基础上 ,提出了一种动态分布式竞争的解释理论 ,用来解释语言神经活动现象和规律 。