{"title":"学习具有异质连接性的脑功能网络,用于脑疾病识别","authors":"","doi":"10.1016/j.neunet.2024.106660","DOIUrl":null,"url":null,"abstract":"<div><p>Functional brain networks (FBNs), which are used to portray interactions between different brain regions, have been widely used to identify potential biomarkers of neurological and mental disorders. The FBNs estimated using current methods tend to be homogeneous, indicating that different brain regions exhibit the same type of correlation. This homogeneity limits our ability to accurately encode complex interactions within the brain. Therefore, to the best of our knowledge, in the present study, for the first time, we propose the existence of heterogeneous FBNs and introduce a novel FBN estimation model that adaptively assigns heterogeneous connections to different pairs of brain regions, thereby effectively encoding the complex interaction patterns in the brain. Specifically, we first construct multiple types of candidate correlations from different views or based on different methods and then develop an improved orthogonal matching pursuit algorithm to select at most one correlation for each brain region pair under the guidance of label information. These adaptively estimated heterogeneous FBNs were then used to distinguish subjects with neurological/mental disorders from healthy controls and identify potential biomarkers related to these disorders. Experimental results on real datasets show that the proposed scheme improves classification performance by 7.07% and 7.58% at the two sites, respectively, compared with the baseline approaches. This emphasizes the plausibility of the heterogeneity hypothesis and effectiveness of the heterogeneous connection assignment algorithm.</p></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Learning functional brain networks with heterogeneous connectivities for brain disease identification\",\"authors\":\"\",\"doi\":\"10.1016/j.neunet.2024.106660\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Functional brain networks (FBNs), which are used to portray interactions between different brain regions, have been widely used to identify potential biomarkers of neurological and mental disorders. The FBNs estimated using current methods tend to be homogeneous, indicating that different brain regions exhibit the same type of correlation. This homogeneity limits our ability to accurately encode complex interactions within the brain. Therefore, to the best of our knowledge, in the present study, for the first time, we propose the existence of heterogeneous FBNs and introduce a novel FBN estimation model that adaptively assigns heterogeneous connections to different pairs of brain regions, thereby effectively encoding the complex interaction patterns in the brain. Specifically, we first construct multiple types of candidate correlations from different views or based on different methods and then develop an improved orthogonal matching pursuit algorithm to select at most one correlation for each brain region pair under the guidance of label information. These adaptively estimated heterogeneous FBNs were then used to distinguish subjects with neurological/mental disorders from healthy controls and identify potential biomarkers related to these disorders. Experimental results on real datasets show that the proposed scheme improves classification performance by 7.07% and 7.58% at the two sites, respectively, compared with the baseline approaches. This emphasizes the plausibility of the heterogeneity hypothesis and effectiveness of the heterogeneous connection assignment algorithm.</p></div>\",\"PeriodicalId\":49763,\"journal\":{\"name\":\"Neural Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0893608024005847\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0893608024005847","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Learning functional brain networks with heterogeneous connectivities for brain disease identification
Functional brain networks (FBNs), which are used to portray interactions between different brain regions, have been widely used to identify potential biomarkers of neurological and mental disorders. The FBNs estimated using current methods tend to be homogeneous, indicating that different brain regions exhibit the same type of correlation. This homogeneity limits our ability to accurately encode complex interactions within the brain. Therefore, to the best of our knowledge, in the present study, for the first time, we propose the existence of heterogeneous FBNs and introduce a novel FBN estimation model that adaptively assigns heterogeneous connections to different pairs of brain regions, thereby effectively encoding the complex interaction patterns in the brain. Specifically, we first construct multiple types of candidate correlations from different views or based on different methods and then develop an improved orthogonal matching pursuit algorithm to select at most one correlation for each brain region pair under the guidance of label information. These adaptively estimated heterogeneous FBNs were then used to distinguish subjects with neurological/mental disorders from healthy controls and identify potential biomarkers related to these disorders. Experimental results on real datasets show that the proposed scheme improves classification performance by 7.07% and 7.58% at the two sites, respectively, compared with the baseline approaches. This emphasizes the plausibility of the heterogeneity hypothesis and effectiveness of the heterogeneous connection assignment algorithm.
期刊介绍:
Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.