高凌云，女，汉族，中共党员，工学博士（生物医学工程专业），博士后（脑科学与脑机接口方向），校聘副教授。主持国自然青年基金项目和省自然青年基金项目各1项，参与国家级、省级科研项目多项，发表SCI论文20余篇。目前主要从事脑健康与认知神经工程、生物医学信号分析、脑机接口及无创神经调控等研究。兼任安徽省人工智能学会智慧医疗专委会委员、安徽省人工智能学会脑机接口专委会委员。
【教育经历】
[1]201009-201406：河南科技大学，生物科学专业，理学学士。
[2]201509-201806：9001诚信金沙，生物物理学专业，理学硕士，导师：叶明全。
[3]201809-202209：浙江大学，生物医学工程专业，工学博士，导师：孙煜。
【工作经历】
[1]202301-202608: 郑州大学/河南省脑科学与脑机接口技术重点实验室，控制科学与工程博士后流动站，博士后，合作导师：尧德中。
[2]202609-至今：皖南医科大学，9001诚信金沙，校聘副教授。
【科研项目】
[1]国家自然科学基金青年科学基金项目（C类），基于多尺度脑网络解码的心理疲劳动态重组机制及HD-tDCS分阶段调控研究（62603622），2026年立项，主持。
[2]河南省自然科学基金青年科学基金项目， 基于静息休息和无创HD-tDCS-EEG闭环调控的心理疲劳高效恢复神经作用机制研究（252300420518），2025年立项，主持。
[3]国家自然科学基金面上项目, TMS-EEG技术促进极端心理疲劳快速恢复的神经机制研究（82172056），2021年立项，参与。
[4]科技创新2030-“脑科学”重大项目，脑机交互计算融合研究平台（2021ZD0200408），2021年立项，参与。
[5]浙江省“领雁”研发攻关计划，AD早期多模态融合预警系统及非侵入性神经调控新技术的研发（2022C03064），2021年立项，参与。
[6]安徽省自然科学基金面上项目，基于MR图像的脑肿瘤计算机辅助诊断关键技术研究（1708085MF142），2017年立项，参与。
[7]教育部人文社科研究规划基金项目，健康大数据下药品不良反应风险评价与预警研究（16YJAZH071），2016年立项，参与。
[8]国家自然科学基金面上项目, 基于粒计算的脑核磁共振图像分割研究（61672386），2016年立项，参与。
[9]安徽省高校自然科学研究重点项目，面向肿瘤基因表达数据的特征选择与集成分类研究（KJ2014A266），2014年立项，参与。
【发表论文】
[1]Pang Y, Zhang Z, Wang X, Pang J, Zhao S, Gao L, Zhang R, Li Z, Wang J*. Brain connectivity mediates the association between childhood maltreatment and personality in young adults[J]. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 2026: 111897.
[2]Gao L, Wang P, Liu Y, Pang Y, Liu G, Qian L, Mei Y*, Zhang R*. Quantifying stability and variability of EEG features across sensory modalities: A comparative analysis of olfactory and audiovisual emotions[J]. Biomedical Signal Processing and Control, 2026, 127: 111213.
[3]Gao L, Zhu L, Wang S, Wu K, Pang Y, Zhang R, Sun Y*. Network-level mechanisms of sustained recovery from mental fatigue differentially modulated by acute exercise and rest[J]. International Journal of Neural Systems, 2026, 36(10):2650038.
[4]Wang S, Xu M*, Qian L, Gao L, Sun Y*. Divergent effects of high-frequency rTMS on cognitive performance in sleep-deprived nurses: An EEG brain network study[J]. Brain Research Bulletin, 2026, 236, 111772.
[5]Yuan J, Xu M, Qian L, Gao L, Sun Y*. Task-specific effects of sleep deprivation on cognitive function and EEG brain network in night-shift nurses[J]. Brain Research Bulletin, 2025, 233, 111661.
[6]Gao L, Xu M, Qian L, Zhang R, Chen M, Hu Y, Li C*, Sun Y*. Identifying individuals with high susceptibility to mental fatigue: A functional connectivity study[J]. NeuroImage, 2025, 324, 121623.
[7]Wu K, Gao L, Feng Z, Kakkos I, Li C, Sun Y*. Multimodal brain network analysis reveals divergent dysconnectivity patterns during mental fatigue: A concurrent EEG-fMRI study[J]. Brain Research Bulletin, 2025, 230 (1), 111505.
[8]Gao L, Zhang R*, Xu M, Sun Y, Li G, Sun Y*. Brain network analysis reveals age-related differences in topological reorganization during vigilance decline[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2025, 33: 3160-3170.
[9]Si Y, Sun Y*, Wu K, Gao L, Wang S, Xu M, Qi X*. Effects of ASMR on mental fatigue recovery revealed by EEG power and brain network analysis[J]. Frontiers in Human Neuroscience, 2025, 19, 1619424.
[10]Pang J, Xu J, Chen L, Teng H, Su C, Zhang Z, Gao L, Zhang R, Liu G, Chen Y, He J, Pang Y*, Li H*. Family history, inflammation, and cerebellum in major depression: a combined VBM and dynamic functional connectivity study[J]. Translational Psychiatry, 2025, 15,1: 188.
[11]Pang Y, Zhao S, Zhang Z, Xu J, Gao L, Zhang R, Li Z, Lu F, Chen H, Wu H, Chen M*, Chen K*, Wang J*. Individual structural covariance connectome reveals aberrant brain developmental trajectories associated with childhood maltreatment[J]. Journal of Psychiatric Research, 2025, 181: 709-715.
[12]Qi P, Zhang X, Kakkos I, Wu K, Wang S, Yuan J, Gao L, Matsopoulos G K, Sun Y*. Individualized prediction of task performance decline using pre-task resting-state functional connectivity[J]. IEEE Journal of Biomedical and Health Informatics, 2023, 27(10): 4971-4982.
[13]Gao L, Yu J, Zhu L, Wang S, Yuan J, Li G, Cai J, Qi X, Sun Y*, Sun Y*. Dynamic reorganization of functional connectivity during post-break task reengagement[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 157-166.
[14]Wang S, Zhu L, Gao L, Yuan J, Li G, Qi P, Sun Y*. Modulating break types induces divergent low band EEG processes during post-break improvement: a power spectral analysis[J]. Frontiers in Human Neuroscience, 2022, 16, 960286.
[15]Gao L, Zhu L, Hu L, Hu H, Wang S, Bezerianos A, Li Y, Li C, Sun Y*. Mid-task physical exercise keeps your mind vigilant: evidences from behavioral performance and EEG functional connectivity[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 31-40.
[16]Wu K, Sun Y, Gao L, Yuan J, Sun Y*. Cross-subject driving fatigue detection model based on feature selection of critical functional connectivity[C]//2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP),IEEE, 2021: 661-666.
[17]Wang H, Liu X, Hu H, Wan Feng, Li T, Gao L, Bezerianos A, Sun Y, Jung T P. Dynamic reorganization of functional connectivity unmasks fatigue related performance declines in simulated driving[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(8): 1790-1799.
[18]Qi P, Gao L, Meng J, Thakor N, Bezerianos A, Sun Y*. Effects of rest-break on mental fatigue recovery determined by a novel temporal brain network analysis of dynamic functional connectivity[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 28(1): 62-71.
[19]Kakkos I, Dimitrakopoulos G N, Gao L, Zhang Y, Qi P, Matsopoulos G K, Thakor N, Bezerianos A, Sun Y*. Mental workload drives different reorganizations of functional cortical connectivity between 2D and 3D simulated flight experiments[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(9): 1704-1713.
[20]Gao L, Bezerianos A, Thakor N, Li J, Sun Y*. Reorganization of temporal brain network underpins accumulative nature of mental fatigue[C]//2019 9th International IEEE/EMBS Conference on Neural Engineering(NER), IEEE, 2019: 494-497.
[21]Qi P, Ru H, Gao L, Zhang X, Zhou T, Tian Y, Thakor N, Bezerianos A, Li J, Sun Y*. Neural mechanisms of mental fatigue revisited: New insights from the brain connectome[J]. Engineering, 2019, 5(2): 276-286.
[22]叶明全, 高凌云, 万春圆. 基于人工蜂群和SVM的基因表达数据分类[J]. 山东大学学报(工学版), 2018, 48(3): 10-16.
[23]叶明全, 高凌云, 伍长荣, 黄道斌, 胡学钢. 基于对称不确定性和邻域粗糙集的肿瘤分类信息基因选择[J]. 数据采集与处理, 2018, 33(3): 426-435.
[24]Gao L, Ye M*, Wu C. Cancer classification based on support vector machine optimized by particle swarm optimization and artificial bee colony[J]. Molecules, 2017, 22(12): 2086.
[25]Gao L, Ye M*, Lu X, Huang D. Hybrid method based on information gain and support vector machine for gene selection in cancer classification[J]. Genomics, Proteomics & Bioinformatics, 2017, 15(6): 389-395.
[26]叶明全, 高凌云, 伍长荣, 万春园. 基于对称不确定性和SVM递归特征消除的信息基因选择方法[J]. 模式识别与人工智能, 2017, 30(5): 429-438.
【参与译著】
[1]《美国睡眠医学会睡眠及其相关事件判读手册: 规则, 术语和技术规范》，浙江大学出版社（2.6版）。