空军军医大学航空航天医学系,710032,西安
西安交通大学生命科学与技术学院,710049,西安
中国人民解放军第94032部队,733003,甘肃武威
中国人民解放军93453部队,038300,山西朔州
作者简介:滕超淋(1988—),男,博士后;
程珊(通信作者),男,副教授,硕士生导师。
收稿:2025-06-19,
纸质出版:2026-03-10
移动端阅览
滕超淋, 吴敏, 黄美清, 等. 虚拟高空暴露下不同程度恐高个体功能脑网络差异性研究[J]. 西安交通大学学报, 2026,60(3):233-240.
TENG Chaolin, WU Min, HUNAG Meiqing, et al. Study on Differences in Functional Brain Networks of Individuals with Varying Degrees of Acrophobia During Virtual Height Exposure[J]. Journal of Xi'an Jiaotong University, 2026, 60(3): 233-240.
滕超淋, 吴敏, 黄美清, 等. 虚拟高空暴露下不同程度恐高个体功能脑网络差异性研究[J]. 西安交通大学学报, 2026,60(3):233-240. DOI: 10.7652/xjtuxb202603022.
TENG Chaolin, WU Min, HUNAG Meiqing, et al. Study on Differences in Functional Brain Networks of Individuals with Varying Degrees of Acrophobia During Virtual Height Exposure[J]. Journal of Xi'an Jiaotong University, 2026, 60(3): 233-240. DOI: 10.7652/xjtuxb202603022.
为探寻不同程度恐高个体脑功能活动差异,利用虚拟现实技术,设计了虚拟高空情景实验范式,记录该情景下的脑电信号,通过精确低分辨率电磁断层扫描和迟延线性相干方法,比较了高空情景下不同程度恐高个体皮层电流密度以及脑网络小世界特征的差异性。研究发现:虚拟高空暴露条件下,不同恐高组皮层电流密度在α频段上存在显著性差异,轻中度恐高组在顶叶(顶下小叶,布罗德曼40区)大于非恐高组,而重度恐高组在额叶小于非恐高组(额中回,布罗德曼6区)和轻中度恐高组(中央前回,布罗德曼10区);恐高组(也就是轻中度恐高和重度恐高)β频段下皮层脑网络小世界度属性显著低于非恐高组,且与恐高回避得分具有显著的负相关,提示脑网络最佳的功能分离和功能整合受到破坏。该研究揭示了虚拟高空暴露下恐高个体改变的皮层功能脑网络特性,有助于加深对恐高症病理机制的理解,为临床恐高的治疗提供了理论依据。
To explore differences in brain function activities among individuals with varying degrees of acrophobia
this study designs a virtual high-altitude scenario experimental paradigm using virtual reality technology and records electroencephalogram signals during this scenario.By applying the exact low-resolution electromagnetic tomography and delayed linear coherence method
this study compares differences in cortical current density and small-world network characteristics of brain networks among individuals with different levels of acrophobia during height exposure.The findings show that under virtual height exposure
significant differences in cortical current density in the α frequency band exist among the groups:the mild-to-moderate acrophobia group shows higher current density in the parietal lobe (inferior parietal lobule
Brodmann area 40)than the non-acrophobia group
whereas the severe acrophobia group exhibits lower current density in the frontal lobe (middle frontal gyrus
Brodmann area 6)compared to the non-acrophobia group
and lower than the mild-to-moderate group in the precentral gyrus (Brodmann area 10).In the β frequency band
the small-worldness property of cortical brain networks is significantly lower in both acrophobia groups (mild-to-moderate and severe)than in the non-acrophobia group and shows a significant negative correlation with acrophobia avoidance scores
suggesting impaired optimal functional segregation and integration in brain networks. This study reveals altered cortical functional brain network characteristics in acrophobic individuals under virtual high-altitude exposure
contributing to a deeper understanding of the pathological mechanisms of acrophobia and providing a theoretical basis for clinical treatment strategies.
HUPPERT D, WUEHR M, BRANDT T.Acrophobia and visual height intolerance:advances in epidemiology and mechanisms[J].Journal of Neurology, 2020, 267(S1):231-240.
LIU Jun, LIN Longnian, WANG D V.Representation of fear of heights by basolateral amygdala neurons[J]. Journal of Neuroscience, 2021, 41(5):1080-1091.
POKORNY L, BESTING L, ROEBRUCK F, et al. Fearful facial expressions reduce inhibition levels in the dorsolateral prefrontal cortex in subjects with specific phobia[J].Depression and Anxiety, 2022, 39 (1 ):26-36.
MAERCKER A, CLOITRE M, BACHEM R, et al. Complex post-traumatic stress disorder[J].Lancet, 2022, 400(10345):60-72.
WANG Qiaoxiu, WANG Hong, HU Fo, et al. Using convolutional neural networks to decode EEG-based functional brain network with different severity of acrophobia[J].Journal of Neural Engineering, 2021, 18 (1):016007.
LANDOWSKA A, ROBERTS D, EACHUS P, et al. Within-and between-session prefrontal cortex response to virtual reality exposure therapy for acrophobia[J]. Frontiers in Human Neuroscience, 2018, 12:362.
HANG Yaming, ZHONG Yuan, ZHANG Guojia, et al. Altered spontaneous neural activity in frontal and visual regions in patients with acrophobia[J].Journal of Affective Disorders, 2022, 303:340-345.
GUO Meilin, ZHONG Yuan, XU Jingren, et al. Altered brain function in patients with acrophobia:a voxel-wise degree centrality analysis[J].Journal of Psychiatric Research, 2023, 164:59-65.
BONANÇA G M, GERHARDT G J L, MOLAN A L, et al. EEG alpha and theta time-frequency structure during a written mathematical task[J].Medical& Biological Engineering & Computing, 2024, 62(6):1869-1885.
MA Zhen, YANG Xinyi, MENG Jiayuan, et al. Decoding arm movement direction using ultra-high-density EEG[J].IEEE Journal of Biomedical and Health Informatics, 2025, 29(6):4035-4045.
CHEN Cheng, FANG Hao, YANG Yuxiao, et al. Model-agnostic meta-learning for EEG-based inter-subject emotion recognition[J].Journal of Neural Engineering, 2025, 22(1):016008.
YANG Hongliu, MÜLLER J, EBERLEIN M, et al. Seizure forecasting with ultra long-term EEG signals[J]. Clinical Neurophysiology, 2024, 167:211-220.
LIN S Y, LIN Chenpei, HSIEH T J, et al. Multiparametric graph theoretical analysis reveals altered structural and functional network topology in Alzheimer’s disease[J].NeuroImage:Clinical, 2019, 22:101680.
WANG Chao, XU Jin, ZHAO Songzhen, et al. Graph theoretical analysis of EEG effective connectivity in vascular dementia patients during a visual oddball task[J]. Clinical Neurophysiology, 2016, 127(1):324-334.
王翘秀,王宏,胡佛,等.基于脑区社团结构的恐高程度识别模型[J].东北大学学报(自然科学版),2021,42(3):381-388.
WANG Qiaoxiu,WANG Hong,HU Fo,et al. Recognition model of fear of heights based on brain region community structure[J].Journal of Northeastern University(Natural Science),2021,42(3):381-388.
MEULEMAN B, RUDRAUF D.Induction and profiling of strong multi-componential emotions in virtual reality[J].IEEE Transactions on Affective Computing, 2021 , 12(1):189-202.
DELORME A, MAKEIG S.EEGLAB:an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis[J].Journal of Neuroscience Methods, 2004, 134(1):9-21.
PASCUAL-MARQUI R D, LEHMANN D, KOUKKOU M, et al. Assessing interactions in the brain with exact low-resolution electromagnetic tomography[J].Philosophical Transactions of the Royal Society:A Mathematical, Physical and Engineering Sciences, 2011, 369(1952):3768-3784.
MAZZIOTTA J, TOGA A, EVANS A, et al. A probabilistic atlas and reference system for the human brain:international consortium for brain mapping (ICBM)[J].Philosophical Transactions of the Royal Society of London:Series B Biological Sciences, 2001 , 356(1412):1293-1322.
FUCHS M, KASTNER J, WAGNER M, et al. A standardized boundary element method volume conductor model[J].Clinical Neurophysiology, 2002, 113 (5):702-712.
CALIANDRO P, VECCHIO F, MIRAGLIA F, et al. Small-world characteristics of cortical connectivity changes in acute stroke[J].Neurorehabilitation and Neural Repair, 2017, 31(1):81-94.
DESIKAN R S, SÉGONNE F, FISCHL B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest[J].NeuroImage, 2006, 31(3):968-980.
JALILI M.Functional brain networks:does the choice of dependency estimator and binarization method matter?[J].Scientific Reports, 2016, 6:29780.
WATTS D J, STROGATZ S H.Collective dynamics of ‘small-world’networks[J].Nature, 1998, 393 (6684):440-442.
TIAN Lixia, WANG Jinhui, YAN Chaogan, et al. Hemisphere-and gender-related differences in smallworld brain networks:a resting-state functional MRI study[J].NeuroImage, 2011, 54(1):191-202.
ACHARD S, BULLMORE E.Efficiency and cost of economical brain functional networks[J].PLoS Computational Biology, 2007, 3(2):e17.
HUMPHRIES M D, GURNEY K, PRESCOTT T J. The brainstem reticular formation is a small-world, not scale-free, network[J].Proceedings of the Royal Society:B Biological Sciences, 2006, 273 (1585 ):503-511.
MASLOV S, SNEPPEN K.Specificity and stability in topology of protein networks[J].Science, 2002, 296 (5569):910-913.
RUBINOV M, SPORNS O.Complex network measures of brain connectivity:uses and interpretations[J]. NeuroImage, 2010, 52(3):1059-1069.
BENJAMINI Y, HOCHBERG Y.Controlling the false discovery rate:a practical and powerful approach to multiple testing[J].Journal of the Royal Statistical Society:Series B (Methodological), 1995, 57(1):289-300.
GARAKH Z, LARIONOVA E, SHMUKLER A, et al. EEG alpha reactivity on eyes opening discriminates patients with schizophrenia and schizoaffective disorder[J]. Clinical Neurophysiology, 2024, 161:211-221.
PRICE J L, DREVETS W C.Neural circuits underlying the pathophysiology of mood disorders[J].Trends in Cognitive Sciences, 2012, 16(1):61-71.
0
浏览量
21
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621