Detecting low-amplitude biomarker activations via decomposition of complex-valued fMRI data with collaborative phase and magnitude sparsity

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jia-Yang Song , Qiu-Hua Lin , Chi Zhou , Yi-Ran Wang , Yu-Ping Wang , Vince D. Calhoun
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引用次数: 0

Abstract

Sparse decomposition of complex-valued functional magnetic resonance imaging (fMRI) data is promising in finding qualified biomarkers for brain disorders such as schizophrenia, by simultaneously using intrinsic spatial sparsity and full functional information of the brain. However, previous methods may miss disease-related low-amplitude activations, since it is challenging to determine if a low-amplitude voxel is signal or noise during the iterative update process based solely on magnitude or phase sparsity. To this end, we propose a novel sparse decomposition model with collaborative phase and magnitude sparsity constraints at the voxel level. Specifically, we impose a sparsity constraint on the product of the magnitude and phase of a voxel above a pre-defined phase threshold. The low-amplitude activations with larger phase changes can survive the update process, despite temporarily violating the small-phase-change characteristic of signal voxels. Moreover, we eliminate phase ambiguity during iterations by proving no additional phase change is introduced by the update rules and by initializing the dictionary matrix atoms using the observed time series with fixed phase angles. We evaluate the proposed method using complex-valued simulated data and experimental resting-state fMRI data from schizophrenia patients and healthy controls. Compared with three state-of-the-art algorithms, the proposed method retains more low-amplitude activations in biomarker regions such as the anterior cingulate cortex and yields sensitive phase maps to disease-related spatial changes. This provides a new tool to estimate an informative fMRI biomarker of mental disorders.
通过分解具有协同相位和量级稀疏性的复杂值fMRI数据来检测低幅度生物标志物激活。
复杂值功能磁共振成像(fMRI)数据的稀疏分解有望同时利用大脑的固有空间稀疏性和完整的功能信息,在寻找精神分裂症等脑部疾病的合格生物标志物方面发挥作用。然而,以前的方法可能会错过与疾病相关的低振幅激活,因为在迭代更新过程中,仅基于幅度或相位稀疏来确定低振幅体素是信号还是噪声是具有挑战性的。为此,我们提出了一种在体素水平上具有协同相位和幅度稀疏约束的新型稀疏分解模型。具体来说,我们对体素的大小和相位的乘积施加了一个稀疏性约束,该约束高于预定义的相位阈值。相位变化较大的低振幅激活虽然暂时违背了信号体素的小相位变化特性,但可以在更新过程中存活下来。此外,我们通过证明更新规则不会引入额外的相位变化和使用观测到的固定相位角的时间序列初始化字典矩阵原子来消除迭代过程中的相位模糊。我们使用来自精神分裂症患者和健康对照的复杂值模拟数据和实验静息状态fMRI数据来评估所提出的方法。与三种最先进的算法相比,该方法在生物标记区域(如前扣带皮层)保留了更多的低振幅激活,并产生了与疾病相关的空间变化的敏感相位图。这为估计精神障碍的fMRI生物标志物提供了一种新的工具。
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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