基于邻域决策粗糙集的脑功能连接生物标记物识别
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作者单位:

北京工业大学

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中图分类号:

TP391.4

基金项目:

国家自然科学基金项目面上项目(61672065),国家自然科学基金青年项目(61906010),北京市教委科技计划一般项目(KM202010005032)


Identifying brain functional connectivity biomarkers based on neighborhood decision rough set
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Affiliation:

Beijing University of Technology

Fund Project:

The General Program of National Natural Science Foundation of China(61672065),The Youth Program of National Natural Science Foundation of China(61906010),The General Program of Science and Technology Plan of Beijing Municipal Education Commission(KM202010005032)

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    摘要:

    脑功能连接判别特征可以作为神经精神类疾病诊断的一种生物标记物,利用机器学习方法对其进行识别是脑科学研究中的一项重要课题.已有脑功能连接生物标记物的识别方法大都忽视了脑功能连接数据高维、连续、多噪声的特性对识别性能的影响,导致所得到的生物标记物的分类能力不强.本文提出了一种基于邻域决策粗糙集的脑功能连接生物标记物识别方法.首先针对脑功能连接数据连续性和高噪声的特点,引入能有效处理连续和高噪声数据的邻域决策粗糙集来识别分类能力更强的脑功能连接判别特征作为生物标记物?其次针对脑功能连接数据的高维特点,通过快速生成邻域和缩小特征搜索空间来保证邻域决策粗糙集识别脑功能连接生物标记物的效率.在 ABIDE I和 ADNI数据集上的实验结果表明,新方法能够准确快速地获得分类能力更强的脑功能连接判别特征,有望为神经精神类疾病的诊断提供更准确的生物标记物.

    Abstract:

    Discriminant characteristics of brain functional connectivity can be used as a biomarker for the diagnosis of neuropsychiatric diseases, using machine learning method to identify it is an important topic in brain science research. Most of the existing recognition methods of brain functional connectivity biomarkers ignore the impact of the characteristics of high-dimensional, continuous and multi noise of brain functional connectivity data on the recognition performance, resulting in the weak classification ability of the obtained biomarkers.This paper proposes a brain function connectivity biomarker recognition method based on neighborhood decision rough set. Firstly, according to the characteristics of continuity and high noise of brain function connectivity data, Neighborhood decision rough set, which can effectively deal with continuous and high noise data, is introduced to identify brain functional connectivity discriminant features with stronger classification ability as biomarkers? secondly, according to the high-dimensional characteristics of brain function connectivity data, the efficiency of identifying brain function connectivity biomarkers by neighborhood decision rough set is guaranteed by quickly generating neighborhood and reducing feature search space.The experimental results on ABIDE I and ADNI data sets show that the new method can accurately and quickly obtain the discriminant features of brain functional connectivity with stronger classification ability, and is expected to provide more accurate biomarkers for the diagnosis of neuropsychiatric diseases.

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  • 收稿日期:2021-09-07
  • 最后修改日期:2022-02-10
  • 录用日期:2022-02-25
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