群信息引导下多主体fMRI数据分析的光滑独立分量分析法。

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yuhui Du, Chen Huang, Vince D Calhoun
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引用次数: 0

摘要

群体独立分量分析(ICA)已被广泛用于从多主体功能磁共振成像(fMRI)数据中提取脑功能网络(FNs)和相关神经影像学指标。然而,fMRI数据中固有的噪声会对ICA的性能产生不利影响,通常会导致有噪声的FNs并阻碍网络级生物标志物的识别。为了解决这一挑战,我们提出了一种新的方法,称为群信息引导光滑独立成分分析(GIG-sICA)。我们的方法有效地生成了平滑的功能网络,降低了噪声,增强了功能一致性,同时保持了FN的主体内独立性和主体间对应性。重要的是,GIG-sICA能够单独或组合处理不同类型的噪声。为了验证我们方法的有效性,我们进行了全面的实验,在模拟和真实的fMRI数据集上比较了GIG-sICA与传统的分组ICA方法。在添加不同类型噪声生成的5个模拟数据集上进行的实验表明,GIG-sICA生成的功能网络更平滑,空间精度更高。此外,对137名精神分裂症患者和144名健康对照者的真实fMRI数据进行的实验表明,GIG-sICA更有效地捕获了功能上有意义的大脑网络,并揭示了更清晰的组间差异。总体而言,GIG-sICA产生平滑和精确的网络估计,支持在神经科学研究的网络水平上发现强大的生物标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Group Information Guided Smooth Independent Component Analysis Method for Multi-Subject fMRI Data Analysis.

Group independent component analysis (ICA) has been extensively used to extract brain functional networks (FNs) and associated neuroimaging measures from multi-subject functional magnetic resonance imaging (fMRI) data. However, the inherent noise in fMRI data can adversely affect the performance of ICA, often leading to noisy FNs and hindering the identification of network-level biomarkers. To address this challenge, we propose a novel method called group information-guided smooth independent component analysis (GIG-sICA). Our method effectively generates smoother functional networks with reduced noise and enhanced functional coherence, while preserving intra-subject independence and inter-subject correspondence of FN. Importantly, GIG-sICA is capable of handling different types of noise either separately or in combination. To validate the efficacy of our approach, we conducted comprehensive experiments, comparing GIG-sICA with traditional group ICA methods on both simulated and real fMRI datasets. Experiments on five simulated datasets, generated by adding various types of noise, demonstrate that GIG-sICA produces smoother functional networks with enhanced spatial accuracy. Additionally, experiments on real fMRI data from 137 schizophrenia patients and 144 healthy controls demonstrate that GIG-sICA more effectively captures functionally meaningful brain networks and reveals clearer group differences. Overall, GIG-sICA produces smooth and precise network estimations, supporting the discovery of robust biomarkers at the network level for neuroscience research.

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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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