Raj Ashish, Sipes Benjamin S, Verma Parul, Mathalon Daniel H, Biswal Bharat, Nagarajan Srikantan
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.
Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, and Veterans Affairs San Francisco Health Care System, San Francisco, CA, United States.
Imaging Neurosci (Camb). 2024 Dec 9;2. doi: 10.1162/imag_a_00381. eCollection 2024.
Resting-state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brain's functional organization and to examine whether it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being underutilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysics-informed signal generation model followed by graph spectral (i.e., eigen) decomposition. We call this model a spectral graph model (SGM) for fMRI, using which we can not only quantify the structure-function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signal's spectral and spatial features into a small number of biophysically interpretable parameters. We expect this model-based analysis of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure-function relationships in a variety of brain disorders.
静息态功能磁共振成像（rs-fMRI）是一种流行且广泛应用的技术，用于探索大脑的功能组织，并检查其在神经或精神疾病中是否发生改变。其最常见的分析方法旨在测量脑区之间的同步波动，即功能连接（FC），通常依赖于不同脑区活动的成对相关性。虽然这些统计图形理论工具在探索与状态和疾病相关的网络改变方面取得了巨大成功，但存在两个关键局限性。首先，它们丢弃了关于fMRI信号丰富频率内容的有用信息。因此，快速多波段采集技术进步所带来的丰富频谱信息未得到充分利用。其次，所分析的功能连接是现象学的，在大脑的潜在结构和过程中没有直接的神经生物学基础。目前尚不存在一个完整的基于全脑静息态功能磁共振成像的生成模型框架，该框架能从其在结构连接组中的潜在生物学基础中获取信息。在此，我们提出一种不同的方法可以同时解决这两个挑战：使用一个适当现实且简约的、基于生物物理学的信号生成模型，然后进行图谱（即特征值）分解。我们将此模型称为fMRI的频谱图模型（SGM），使用该模型，我们不仅可以量化个体受试者的结构 - 功能关系，还可以将fMRI信号频谱和空间特征的可变且个体特异性的全部信息浓缩为少量具有生物物理学可解释性的参数。我们期望这种基于模型的rs-fMRI分析能够无缝地与结构整合，用于检查各种脑部疾病中结构 - 功能关系的状态和特质特征。
