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作者:

Lv, Han (Lv, Han.) | Liu, Jinduo (Liu, Jinduo.) | Chen, Qian (Chen, Qian.) | Zhang, Zuozhen (Zhang, Zuozhen.) | Wang, Zhaodi (Wang, Zhaodi.) | Gong, Shusheng (Gong, Shusheng.) | Ji, Junzhong (Ji, Junzhong.) (学者:冀俊忠) | Wang, Zhenchang (Wang, Zhenchang.)

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摘要:

Tinnitus is associated with abnormal functional connectivity of multiple regions of the brain. However, previous analytic methods have disregarded information on the direction of functional connectivity, leading to only a moderate efficacy of pretreatment planning. We hypothesized that the pattern of directional functional connectivity can provide key information on treatment outcomes. Sixty-four participants were enrolled in this study: eighteen patients with tinnitus were categorized into the effective group, twenty-two patients into the ineffective group, and twenty-four healthy participants into the healthy control group. We acquired resting-state functional magnetic resonance images prior to sound therapy and constructed an effective connectivity network of the three groups using an artificial bee colony algorithm and transfer entropy. The key feature of patients with tinnitus was the significantly increased signal output of the sensory network, including the auditory, visual, and somatosensory networks, and parts of the motor network. This provided critical insights into the gain theory of tinnitus development. The altered pattern of functional information orchestration, represented by a higher degree of hypervigilance-driven attention and enhanced multisensory integration, may explain poor clinical outcomes. The activated gating function of the thalamus is one of the key factors for a good prognosis in tinnitus treatment. We developed a novel method for analyzing effective connectivity, facilitating an understanding of the tinnitus mechanism and treatment outcome expectation based on the direction of information flow.

关键词:

Biomedical imaging brain network Hospitals Measurement Time series analysis Medical treatment effective connectivity Tinnitus sound therapy Artificial bee colony algorithm fMRI Functional magnetic resonance imaging

作者机构:

  • [ 1 ] [Lv, Han]Capital Med Univ, Beijing Friendship Hosp, Dept Radiol, Beijing 100050, Peoples R China
  • [ 2 ] [Chen, Qian]Capital Med Univ, Beijing Friendship Hosp, Dept Radiol, Beijing 100050, Peoples R China
  • [ 3 ] [Wang, Zhenchang]Capital Med Univ, Beijing Friendship Hosp, Dept Radiol, Beijing 100050, Peoples R China
  • [ 4 ] [Liu, Jinduo]Beijing Univ Technol, Coll Comp Sci & Technol, Fac Informat Technol, Beijing Municipal Key Lab Multimedia & Intelligent, Beijing 100124, Peoples R China
  • [ 5 ] [Zhang, Zuozhen]Beijing Univ Technol, Coll Comp Sci & Technol, Fac Informat Technol, Beijing Municipal Key Lab Multimedia & Intelligent, Beijing 100124, Peoples R China
  • [ 6 ] [Ji, Junzhong]Beijing Univ Technol, Coll Comp Sci & Technol, Fac Informat Technol, Beijing Municipal Key Lab Multimedia & Intelligent, Beijing 100124, Peoples R China
  • [ 7 ] [Wang, Zhaodi]Capital Med Univ, Beijing Friendship Hosp, Dept Otolaryngol Head & Neck Surg, Beijing 100050, Peoples R China
  • [ 8 ] [Gong, Shusheng]Capital Med Univ, Beijing Friendship Hosp, Dept Otolaryngol Head & Neck Surg, Beijing 100050, Peoples R China

通讯作者信息:

  • [Wang, Zhenchang]Capital Med Univ, Beijing Friendship Hosp, Dept Radiol, Beijing 100050, Peoples R China;;[Ji, Junzhong]Beijing Univ Technol, Coll Comp Sci & Technol, Fac Informat Technol, Beijing Municipal Key Lab Multimedia & Intelligent, Beijing 100124, Peoples R China;;

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来源 :

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING

ISSN: 1534-4320

年份: 2023

卷: 31

页码: 1158-1166

4 . 9 0 0

JCR@2022

ESI学科: ENGINEERING;

ESI高被引阀值:19

被引次数:

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SCOPUS被引频次: 6

ESI高被引论文在榜: 0 展开所有

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