Suppr超能文献

联合约束组稀疏连通性表示法可改善基于常规获取的T1加权成像脑网络的阿尔茨海默病早期诊断。

Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer's disease on routinely acquired T1-weighted imaging-based brain network.

作者信息

Zhu Chuanzhen, Li Honglun, Song Zhiwei, Jiang Minbo, Song Limei, Li Lin, Wang Xuan, Zheng Qiang

机构信息

School of Computer and Control Engineering, Yantai University, No 30, Qingquan Road, Laishan District, Yantai, 264005 Shandong China.

Departments of Medical Oncology and Radiology, Affiliated Yantai Yuhuangding Hospital of Qingdao University Medical College, Yantai, 264099 China.

出版信息

Health Inf Sci Syst. 2024 Mar 6;12(1):19. doi: 10.1007/s13755-023-00269-0. eCollection 2024 Dec.

Abstract

BACKGROUND

Radiomics-based morphological brain networks (radMBN) constructed from routinely acquired structural MRI (sMRI) data have gained attention in Alzheimer's disease (AD). However, the radMBN suffers from limited characterization of AD because sMRI only characterizes anatomical changes and is not a direct measure of neuronal pathology or brain activity.

PURPOSE

To establish a group sparse representation of the radMBN under a joint constraint of group-level white matter fiber connectivity and individual-level sMRI regional similarity (JCGS-radMBN).

METHODS

Two publicly available datasets were adopted, including 120 subjects from ADNI with both T1-weighted image (T1WI) and diffusion MRI (dMRI) for JCGS-radMBN construction, 818 subjects from ADNI and 200 subjects solely with T1WI from AIBL for validation in early AD diagnosis. Specifically, the JCGS-radMBN was conducted by jointly estimating non-zero connections among subjects, with the regularization term constrained by group-level white matter fiber connectivity and individual-level sMRI regional similarity. Then, a triplet graph convolutional network was adopted for early AD diagnosis. The discriminative brain connections were identified using a two-sample t-test, and the neurobiological interpretation was validated by correlating the discriminative brain connections with cognitive scores.

RESULTS

The JCGS-radMBN exhibited superior classification performance over five brain network construction methods. For the typical NC vs. AD classification, the JCGS-radMBN increased by 1-30% in accuracy over the alternatives on ADNI and AIBL. The discriminative brain connections exhibited a strong connectivity to hippocampus, parahippocampal gyrus, and basal ganglia, and had significant correlation with MMSE scores.

CONCLUSION

The proposed JCGS-radMBN facilitated the AD characterization of brain network established on routinely acquired imaging modality of sMRI.

SUPPLEMENTARY INFORMATION

The online version of this article (10.1007/s13755-023-00269-0) contains supplementary material, which is available to authorized users.

摘要

背景

基于常规采集的结构磁共振成像(sMRI)数据构建的基于放射组学的形态学脑网络(radMBN)在阿尔茨海默病(AD)研究中受到关注。然而,radMBN对AD的特征描述有限,因为sMRI仅能表征解剖学变化,并非神经元病理学或脑活动的直接测量指标。

目的

在组水平白质纤维连通性和个体水平sMRI区域相似性的联合约束下建立radMBN的组稀疏表示(JCGS-radMBN)。

方法

采用两个公开可用的数据集,其中来自阿尔茨海默病神经成像计划(ADNI)的120名受试者同时拥有T1加权图像(T1WI)和扩散磁共振成像(dMRI)用于构建JCGS-radMBN,来自ADNI的818名受试者以及来自澳大利亚成像生物标志物和生活方式旗舰研究衰老队列(AIBL)的仅拥有T1WI的200名受试者用于早期AD诊断的验证。具体而言,通过联合估计受试者之间的非零连接来构建JCGS-radMBN,正则化项受组水平白质纤维连通性和个体水平sMRI区域相似性的约束。然后,采用三元图卷积网络进行早期AD诊断。使用双样本t检验识别有鉴别力的脑连接,并通过将有鉴别力的脑连接与认知评分相关联来验证神经生物学解释。

结果

JCGS-radMBN在五种脑网络构建方法中表现出卓越的分类性能。对于典型的正常对照(NC)与AD分类,JCGS-radMBN在ADNI和AIBL数据集上的准确率比其他方法提高了1%至30%。有鉴别力的脑连接与海马体、海马旁回和基底神经节表现出强烈的连通性,并且与简易精神状态检查表(MMSE)评分有显著相关性。

结论

所提出的JCGS-radMBN有助于对基于常规采集的sMRI成像模态建立的脑网络进行AD特征描述。

补充信息

本文的在线版本(10.1007/s13755-023-00269-0)包含补充材料,授权用户可获取。

相似文献

7
Selegiline for Alzheimer's disease.司来吉兰用于治疗阿尔茨海默病。
Cochrane Database Syst Rev. 2003(1):CD000442. doi: 10.1002/14651858.CD000442.

引用本文的文献

本文引用的文献

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验