现代电子技术2026，Vol.49Issue(13)：141-148,8.DOI:10.16652/j.issn.1004-373X.2026.13.021
面孔重复抑制的fMRI快速因果模型选择
fMRI fast causal model selection for face repetition suppression
摘要
Abstract
This paper focuses on the causal modulation of brain area connections and the computational efficiency of algorithms for the problem of repetition suppression(RS)in face recognition.It focuses on the modulation effect of immediate and delayed repetitions on the forward and backward connections of brain areas,and proposes a fast dynamic causal model selection(DCMS)algorithm.The computational complexity is greatly reduced by sparse variational inference and linear regression modeling,and the random effect analysis method is used to maintain the model selection performance similar to the traditional method.The experimental data comes from the public data of the open functional magnetic resonance imaging(openfMRI)library.The results show that the computational time of the proposed algorithm is only 6%～10%of the traditional method,but the selected model is consistent with the traditional algorithm;and it is found that when the interference caused by the modulation effect of face perception and recognition is reduced,the modulation effects of immediate repetition and delayed repetition are similar,and there is only a difference in intensity.This is different from the traditional view,indicating that the brain adapts to information processing needs by flexibly adjusting the connection strength at different time scales by the bidirectional feedback and regulation of neurons,rather than relying on independent subsystems.To sum up,the proposed algorithm provides a new idea for the treatment of cognitive disorders.关键词
重复抑制/fMRI/模型选择/稀疏变分推断/时间复杂度/动态因果模型Key words
repetition suppression/fMRI/model selection/sparse variational inference/time complexity/dynamic causal model分类
信息技术与安全科学引用本文复制引用
吴海锋,李冉,胡新航,曾玉..面孔重复抑制的fMRI快速因果模型选择[J].现代电子技术,2026,49(13):141-148,8.基金项目
国家自然科学基金资助项目(62161052) （62161052）
云南省教育厅科学研究基金项目(2024Y432) （2024Y432）
