基于邻域决策粗糙集的脑功能连接生物标记物识别

Identifying brain functional connectivity biomarkers based on neighborhood decision rough set

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.对此,提出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,所提出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.

Discriminant characteristics of brain functional connectivity can be used as a biomarker for the diagnosis of neuropsychiatric diseases.Using a machine learning method to identify 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 sets.Firstly,according to the characteristics of continuity and high noise of brain function connectivity data,a 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.Then,according to the high-dimensional characteristics of brain function connectivity data,the efficiency of identifying brain function connectivity biomarkers by the neighborhood decision rough set is guaranteed by quickly generating the neighborhood and reducing the feature search space.The experimental results on the ABIDE I and ADNI data sets show that the proposed 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.