At present, the diagnosis of brain disease is mainly based on the self-reported symptoms and clinical signs of the patient, which can easily lead to psychiatrists' bias. The purpose of this study is to develop a brain network clustering model to accurately identify brain diseases based on resting state functional magnetic resonance imaging (fMRI) in the absence of clinical information. We use cosine similarity and sub-network kernels to measure attribute similarity and structure similarity, respectively. By integrating the structure similarity and attribute similarity into one matrix, spectral clustering is used to achieve brain network clustering. Finally, we evaluate this method on three diseases: Alzheimer's disease, Bipolar disorder patients, and Schizophrenia. The performance of methods is evaluated by measuring clustering consistency. Clustering consistency is similar to clustering accuracy, which is used to evaluate the consistency between the clustering labels and clinical diagnostic labels of the subjects. The experimental results show that our proposed method can significantly improve clustering performance, with a consistency of 60.6% for Alzheimer's disease, with a consistency of 100% for Schizophrenia, with a consistency of 100% for Bipolar disorder patients.

中文翻译：

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基于属性和结构信息的脑功能网络聚类及其在脑疾病中的应用


目前，脑部疾病的诊断主要根据患者自述的症状和临床体征，这很容易导致精神科医生的偏见。本研究的目的是开发一种脑网络聚类模型，在缺乏临床信息的情况下基于静息态功能磁共振成像（fMRI）准确识别脑部疾病。我们使用余弦相似度和子网络核分别测量属性相似度和结构相似度。通过将结构相似性和属性相似性整合到一个矩阵中，利用谱聚类来实现脑网络聚类。最后，我们在三种疾病上评估了这种方法：阿尔茨海默病、双相情感障碍患者和精神分裂症。通过测量聚类一致性来评估方法的性能。聚类一致性与聚类准确率类似，用于评价受试者的聚类标签与临床诊断标签的一致性。实验结果表明，我们提出的方法可以显着提高聚类性能，对于阿尔茨海默病的一致性为60.6％，对于精神分裂症的一致性为100％，对于双相情感障碍患者的一致性为100％。