Deng Jianjun, Sun Jingwen, Lu Shuangshuang, Yue Kecen, Liu Wenjia, Shi Haifeng, Zou Ling
School of Computer and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu 213164, China.
The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Department of Radiology, China.
Behav Brain Res. 2023 Apr 12;443:114325. doi: 10.1016/j.bbr.2023.114325. Epub 2023 Feb 2.
Although MRI has made considerable progress in Inflammatory bowel disease (IBD), most studies have concentrated on data information from a single modality, and a better understanding of the interplay between brain function and structure, as well as appropriate clinical aids to diagnosis, is required. We calculated functional connectivity through fMRI time series using resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion kurtosis imaging (DKI) data from 27 IBD patients and 29 healthy controls. Through the DKI data of each subject, its unique structure map is obtained, and the relevant indicators are projected onto the structure map corresponding to each subject by using the graph Fourier transform in the grasp signal processing (GSP) technology. After the features are optimized, a classical support vector machine is used to classify the features. IBD patients have altered functional connectivity in the default mode network (DMN) and subcortical network (SCN). At the same time, compared with the traditional brain network analysis, in the test of some indicators, the average classification accuracy produced by the framework method is 12.73% higher than that of the traditional analysis method. This paper found that the brain network structure of IBD patients in DMN and SCN has changed. Simultaneously, the application of GSP technology to fuse functional information and structural information is superior to the traditional framework in classification, providing a new perspective for subsequent clinical auxiliary diagnosis.
尽管磁共振成像（MRI）在炎症性肠病（IBD）方面取得了显著进展，但大多数研究都集中在单一模态的数据信息上，仍需要更好地理解脑功能与结构之间的相互作用以及合适的临床诊断辅助手段。我们使用静息态功能磁共振成像（rs-fMRI）和扩散峰度成像（DKI）数据，对27例IBD患者和29名健康对照者通过功能磁共振成像时间序列计算功能连接性。通过每个受试者的DKI数据，获得其独特的结构图谱，并利用信号处理（GSP）技术中的图形傅里叶变换将相关指标投影到每个受试者对应的结构图谱上。在对特征进行优化后，使用经典支持向量机对特征进行分类。IBD患者在默认模式网络（DMN）和皮质下网络（SCN）中的功能连接性发生了改变。同时，与传统脑网络分析相比，在一些指标的测试中，框架方法产生的平均分类准确率比传统分析方法高12.73%。本文发现IBD患者在DMN和SCN中的脑网络结构发生了变化。同时，GSP技术融合功能信息和结构信息在分类方面优于传统框架，为后续临床辅助诊断提供了新的视角。
