Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,n...Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,neural oscillatory dynamics,and brain network reorganization remain unclear.This investigation seeks to systematically evaluate the therapeutic potential of rTMS as a non-invasive neuromodulatory intervention through a multimodal framework integrating clinical assessments,molecular profiling,and neurophysiological monitoring.Methods In this prospective double-blind trial,12 AD patients underwent a 14-day protocol of 20 Hz rTMS,with comprehensive multimodal assessments performed pre-and postintervention.Cognitive functioning was quantified using the mini-mental state examination(MMSE)and Montreal cognitive assessment(MOCA),while daily living capacities and neuropsychiatric profiles were respectively evaluated through the activities of daily living(ADL)scale and combined neuropsychiatric inventory(NPI)-Hamilton depression rating scale(HAMD).Peripheral blood biomarkers,specifically Aβ1-40 and phosphorylated tau(p-tau181),were analyzed to investigate the effects of rTMS on molecular metabolism.Spectral power analysis was employed to investigate rTMS-induced modulations of neural rhythms in AD patients,while brain network analyses incorporating topological properties were conducted to examine stimulus-driven network reorganization.Furthermore,systematic assessment of correlations between cognitive scale scores,blood biomarkers,and network characteristics was performed to elucidate cross-modal therapeutic associations.Results Clinically,MMSE and MOCA scores improved significantly(P<0.05).Biomarker showed that Aβ1-40 level increased(P<0.05),contrasting with p-tau181 reduction.Moreover,the levels of Aβ1-40 were positively correlated with MMSE and MOCA scores.Post-intervention analyses revealed significant modulations in oscillatory power,characterized by pronounced reductions in delta(P<0.05)and theta bands(P<0.05),while concurrent enhancements were observed in alpha,beta,and gamma band activities(all P<0.05).Network analysis revealed frequency-specific reorganization:clustering coefficients were significantly decreased in delta,theta,and alpha bands(P<0.05),while global efficiency improvement was exclusively detected in the delta band(P<0.05).The alpha band demonstrated concurrent increases in average nodal degree(P<0.05)and characteristic path length reduction(P<0.05).Further research findings indicate that the changes in the clinical scale HAMD scores before and after rTMS stimulation are negatively correlated with the changes in the blood biomarkers Aβ1-40 and p-tau181.Additionally,the changes in the clinical scales MMSE and MoCA scores were negatively correlated with the changes in the node degree of the alpha frequency band and negatively correlated with the clustering coefficient of the delta frequency band.However,the changes in MMSE scores are positively correlated with the changes in global efficiency of both the delta and alpha frequency bands.Conclusion 20 Hz rTMS targeting dorsolateral prefrontal cortex(DLPFC)significantly improves cognitive function and enhances the metabolic clearance ofβ-amyloid and tau proteins in AD patients.This neurotherapeutic effect is mechanistically associated with rTMS-mediated frequency-selective neuromodulation,which enhances the connectivity of oscillatory networks through improved neuronal synchronization and optimized topological organization of functional brain networks.These findings not only support the efficacy of rTMS as an adjunctive therapy for AD but also underscore the importance of employing multiple assessment methods—including clinical scales,blood biomarkers,and EEG——in understanding and monitoring the progression of AD.This research provides a significant theoretical foundation and empirical evidence for further exploration of rTMS applications in AD treatment.展开更多
Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function...Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function.Besides,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical properties.Whether white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical properties.As such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional dependence.Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging.Instead,this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue.We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach.The matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths.The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures.Finally,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces.Compared with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling efficiency.The predictions made by our trained model demonstrate sufficient accuracy.Specifically,we find that the level of internal blood pressure can greatly influence stress distribution and determine the possible related damage behaviors.展开更多
Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby c...Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby contributing to the advancement of camouflage evaluation.In this study,images with various camouflage effects were presented to observers to generate electroencephalography(EEG)signals,which were then used to construct a brain functional network.The topological parameters of the network were subsequently extracted and input into a machine learning model for training.The results indicate that most of the classifiers achieved accuracy rates exceeding 70%.Specifically,the Logistic algorithm achieved an accuracy of 81.67%.Therefore,it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability.The proposed method fully considers the aspects of human visual and cognitive processes,overcomes the subjectivity of human interpretation,and achieves stable and reliable accuracy.展开更多
目的分析急性脑梗死(acute cerebral infarction,ACI)患者小而密低密度脂蛋白(small dense low-density lipoprotein,sd-LDL)及脂蛋白(a)水平与颈动脉斑块稳定性的关系。方法回顾性选取2020年2月至2024年2月河北省人民医院收治的老年AC...目的分析急性脑梗死(acute cerebral infarction,ACI)患者小而密低密度脂蛋白(small dense low-density lipoprotein,sd-LDL)及脂蛋白(a)水平与颈动脉斑块稳定性的关系。方法回顾性选取2020年2月至2024年2月河北省人民医院收治的老年ACI患者160例,所有患者行颈部彩色多普勒超声检查,依据颈动脉斑块情况分为无斑块组43例、稳定斑块组56例和不稳定斑块组61例,另取同期河北省人民医院健康体检者40例作为对照组,比较4组临床资料、sd-LDL、脂蛋白(a)水平,评估sd-LDL、脂蛋白(a)水平对不稳定斑块的预测价值。结果与无斑块组比较,稳定斑块组和不稳定斑块组美国国立卫生研究院卒中量表(National Institute of Health Stroke Scale,NIHSS)评分、低密度脂蛋白胆固醇(low density lipoprotein cholesterol,LDL-C)显著升高,不稳定斑块组总胆固醇显著升高,高密度脂蛋白胆固醇(high density lipoprotein cholesterol,HDL-C)显著降低,对照组NIHSS评分、总胆固醇、三酰甘油、LDL-C显著降低,HDL-C显著升高,差异有统计学意义(P<0.05);与稳定斑块组比较,不稳定斑块组NIHSS评分、LDL-C、sdLDL、脂蛋白(a)显著升高,HDL-C显著降低,对照组脂蛋白(a)显著降低,差异有统计学意义(P<0.05)。Pearson相关性分析显示,sd-LDL、脂蛋白(a)水平与NIHSS评分、总胆固醇、三酰甘油、LDL-C呈显著正相关(P<0.05,P<0.01),与HDL-C呈显著负相关(P<0.01)。二元logistic回归分析显示,NIHSS评分、LDL-C、sd-LDL、脂蛋白(a)是ACI患者颈动脉不稳定斑块形成的危险因素,HDL-C是保护因素(P<0.01)。ROC曲线结果显示,sd-LDL、脂蛋白(a)及联合检测对颈动脉斑块稳定性预测的ROC曲线下面积分别为0.830、0.847、0.921,联合检测的敏感性高于sd-LDL、脂蛋白(a)单项指标检测(93.44%vs 88.52%、86.89%,P=0.000)。结论血浆sd-LDL、脂蛋白(a)水平与ACI患者颈动脉斑块稳定性具有一定关联性,可作为临床相关参考指标。展开更多
基于聚焦性能对经颅磁线圈的影响,针对聚焦型线圈商用不足的问题,设计一种具有高聚焦性的单通道双梯形双层线圈。首先使用球头模型分析不同尺寸、相同结构的线圈性能,得到较优的线圈尺寸;为进一步提升聚焦度,再以中尺寸线圈为研究目标,...基于聚焦性能对经颅磁线圈的影响,针对聚焦型线圈商用不足的问题,设计一种具有高聚焦性的单通道双梯形双层线圈。首先使用球头模型分析不同尺寸、相同结构的线圈性能,得到较优的线圈尺寸;为进一步提升聚焦度,再以中尺寸线圈为研究目标,探讨五种不同结构的双层线圈与两种商用8字形线圈的性能差异,得到最优线圈的结构;最后使用50组存在个体差异性的真实脑模型进行仿真验证,分析球头模型结果的可靠性。仿真结果表明:使用球头模型时,优化后的新型线圈对比70 mm figure-8 coil的聚焦度提升了69.48%,刺激深度减少了27.18%;对比25 mm figure-8 coil的聚焦度提升了44.78%,刺激深度减少了8.5%;使用50组真实脑模型时,优化后的新型线圈对比70 mm figure-8 coil的聚焦度提升了62.07%,刺激深度减少了25.71%;对比25 mm figure-8 coil的聚焦度提升了39.49%,刺激深度减少了9.5%。两种模型仿真数据结果趋于一致,证实了仿真可靠性的同时也证明了新型线圈具有更强的刺激强度和聚焦度,能大大提升TMS治疗的安全性,减少不适感,同时单通道设计易于实现,具有较高的性能优势。展开更多
文摘Objective Repetitive transcranial magnetic stimulation(rTMS)has demonstrated efficacy in enhancing neurocognitive performance in Alzheimer’s disease(AD),but the neurobiological mechanisms linking synaptic pathology,neural oscillatory dynamics,and brain network reorganization remain unclear.This investigation seeks to systematically evaluate the therapeutic potential of rTMS as a non-invasive neuromodulatory intervention through a multimodal framework integrating clinical assessments,molecular profiling,and neurophysiological monitoring.Methods In this prospective double-blind trial,12 AD patients underwent a 14-day protocol of 20 Hz rTMS,with comprehensive multimodal assessments performed pre-and postintervention.Cognitive functioning was quantified using the mini-mental state examination(MMSE)and Montreal cognitive assessment(MOCA),while daily living capacities and neuropsychiatric profiles were respectively evaluated through the activities of daily living(ADL)scale and combined neuropsychiatric inventory(NPI)-Hamilton depression rating scale(HAMD).Peripheral blood biomarkers,specifically Aβ1-40 and phosphorylated tau(p-tau181),were analyzed to investigate the effects of rTMS on molecular metabolism.Spectral power analysis was employed to investigate rTMS-induced modulations of neural rhythms in AD patients,while brain network analyses incorporating topological properties were conducted to examine stimulus-driven network reorganization.Furthermore,systematic assessment of correlations between cognitive scale scores,blood biomarkers,and network characteristics was performed to elucidate cross-modal therapeutic associations.Results Clinically,MMSE and MOCA scores improved significantly(P<0.05).Biomarker showed that Aβ1-40 level increased(P<0.05),contrasting with p-tau181 reduction.Moreover,the levels of Aβ1-40 were positively correlated with MMSE and MOCA scores.Post-intervention analyses revealed significant modulations in oscillatory power,characterized by pronounced reductions in delta(P<0.05)and theta bands(P<0.05),while concurrent enhancements were observed in alpha,beta,and gamma band activities(all P<0.05).Network analysis revealed frequency-specific reorganization:clustering coefficients were significantly decreased in delta,theta,and alpha bands(P<0.05),while global efficiency improvement was exclusively detected in the delta band(P<0.05).The alpha band demonstrated concurrent increases in average nodal degree(P<0.05)and characteristic path length reduction(P<0.05).Further research findings indicate that the changes in the clinical scale HAMD scores before and after rTMS stimulation are negatively correlated with the changes in the blood biomarkers Aβ1-40 and p-tau181.Additionally,the changes in the clinical scales MMSE and MoCA scores were negatively correlated with the changes in the node degree of the alpha frequency band and negatively correlated with the clustering coefficient of the delta frequency band.However,the changes in MMSE scores are positively correlated with the changes in global efficiency of both the delta and alpha frequency bands.Conclusion 20 Hz rTMS targeting dorsolateral prefrontal cortex(DLPFC)significantly improves cognitive function and enhances the metabolic clearance ofβ-amyloid and tau proteins in AD patients.This neurotherapeutic effect is mechanistically associated with rTMS-mediated frequency-selective neuromodulation,which enhances the connectivity of oscillatory networks through improved neuronal synchronization and optimized topological organization of functional brain networks.These findings not only support the efficacy of rTMS as an adjunctive therapy for AD but also underscore the importance of employing multiple assessment methods—including clinical scales,blood biomarkers,and EEG——in understanding and monitoring the progression of AD.This research provides a significant theoretical foundation and empirical evidence for further exploration of rTMS applications in AD treatment.
文摘Brain tissue is one of the softest parts of the human body,composed of white matter and grey matter.The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function.Besides,traumatic brain injury(TBI)and various brain diseases are also greatly influenced by the brain's mechanical properties.Whether white matter or grey matter,brain tissue contains multiscale structures composed of neurons,glial cells,fibers,blood vessels,etc.,each with different mechanical properties.As such,brain tissue exhibits complex mechanical behavior,usually with strong nonlinearity,heterogeneity,and directional dependence.Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging.Instead,this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue.We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach.The matrix is described by an isotropic or anisotropic nonlinear elastic model.A representative unit cell(RUC)with blood vessels is built,which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths.The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures.Finally,the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces.Compared with a direct numerical simulation that employs a reference material model,our proposed approach greatly reduces the computational cost and improves modeling efficiency.The predictions made by our trained model demonstrate sufficient accuracy.Specifically,we find that the level of internal blood pressure can greatly influence stress distribution and determine the possible related damage behaviors.
基金sponsored by the National Defense Science and Technology Key Laboratory Fund(Grant No.61422062205)the Equipment Pre-Research Fund(Grant No.JCKYS2022LD9)。
文摘Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby contributing to the advancement of camouflage evaluation.In this study,images with various camouflage effects were presented to observers to generate electroencephalography(EEG)signals,which were then used to construct a brain functional network.The topological parameters of the network were subsequently extracted and input into a machine learning model for training.The results indicate that most of the classifiers achieved accuracy rates exceeding 70%.Specifically,the Logistic algorithm achieved an accuracy of 81.67%.Therefore,it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability.The proposed method fully considers the aspects of human visual and cognitive processes,overcomes the subjectivity of human interpretation,and achieves stable and reliable accuracy.
文摘目的分析急性脑梗死(acute cerebral infarction,ACI)患者小而密低密度脂蛋白(small dense low-density lipoprotein,sd-LDL)及脂蛋白(a)水平与颈动脉斑块稳定性的关系。方法回顾性选取2020年2月至2024年2月河北省人民医院收治的老年ACI患者160例,所有患者行颈部彩色多普勒超声检查,依据颈动脉斑块情况分为无斑块组43例、稳定斑块组56例和不稳定斑块组61例,另取同期河北省人民医院健康体检者40例作为对照组,比较4组临床资料、sd-LDL、脂蛋白(a)水平,评估sd-LDL、脂蛋白(a)水平对不稳定斑块的预测价值。结果与无斑块组比较,稳定斑块组和不稳定斑块组美国国立卫生研究院卒中量表(National Institute of Health Stroke Scale,NIHSS)评分、低密度脂蛋白胆固醇(low density lipoprotein cholesterol,LDL-C)显著升高,不稳定斑块组总胆固醇显著升高,高密度脂蛋白胆固醇(high density lipoprotein cholesterol,HDL-C)显著降低,对照组NIHSS评分、总胆固醇、三酰甘油、LDL-C显著降低,HDL-C显著升高,差异有统计学意义(P<0.05);与稳定斑块组比较,不稳定斑块组NIHSS评分、LDL-C、sdLDL、脂蛋白(a)显著升高,HDL-C显著降低,对照组脂蛋白(a)显著降低,差异有统计学意义(P<0.05)。Pearson相关性分析显示,sd-LDL、脂蛋白(a)水平与NIHSS评分、总胆固醇、三酰甘油、LDL-C呈显著正相关(P<0.05,P<0.01),与HDL-C呈显著负相关(P<0.01)。二元logistic回归分析显示,NIHSS评分、LDL-C、sd-LDL、脂蛋白(a)是ACI患者颈动脉不稳定斑块形成的危险因素,HDL-C是保护因素(P<0.01)。ROC曲线结果显示,sd-LDL、脂蛋白(a)及联合检测对颈动脉斑块稳定性预测的ROC曲线下面积分别为0.830、0.847、0.921,联合检测的敏感性高于sd-LDL、脂蛋白(a)单项指标检测(93.44%vs 88.52%、86.89%,P=0.000)。结论血浆sd-LDL、脂蛋白(a)水平与ACI患者颈动脉斑块稳定性具有一定关联性,可作为临床相关参考指标。
文摘基于聚焦性能对经颅磁线圈的影响,针对聚焦型线圈商用不足的问题,设计一种具有高聚焦性的单通道双梯形双层线圈。首先使用球头模型分析不同尺寸、相同结构的线圈性能,得到较优的线圈尺寸;为进一步提升聚焦度,再以中尺寸线圈为研究目标,探讨五种不同结构的双层线圈与两种商用8字形线圈的性能差异,得到最优线圈的结构;最后使用50组存在个体差异性的真实脑模型进行仿真验证,分析球头模型结果的可靠性。仿真结果表明:使用球头模型时,优化后的新型线圈对比70 mm figure-8 coil的聚焦度提升了69.48%,刺激深度减少了27.18%;对比25 mm figure-8 coil的聚焦度提升了44.78%,刺激深度减少了8.5%;使用50组真实脑模型时,优化后的新型线圈对比70 mm figure-8 coil的聚焦度提升了62.07%,刺激深度减少了25.71%;对比25 mm figure-8 coil的聚焦度提升了39.49%,刺激深度减少了9.5%。两种模型仿真数据结果趋于一致,证实了仿真可靠性的同时也证明了新型线圈具有更强的刺激强度和聚焦度,能大大提升TMS治疗的安全性,减少不适感,同时单通道设计易于实现,具有较高的性能优势。
