Morante Manuel, Frølich Kristian, Rehman Naveed Ur
Department of Electrical and Computer Engineering of Aarhus University, Aarhus, Denmark.
Front Neurosci. 2025 Aug 28;19:1653007. doi: 10.3389/fnins.2025.1653007. eCollection 2025.
This paper explores an alternative way for analyzing static Functional Connectivity (FC) in functional Magnetic Resonance Imaging (fMRI) data across multiple timescales using a class of adaptive frequency-based methods referred to as Multivariate Mode Decomposition (MMD). The proposed method decomposes fMRI into their intrinsic multivariate oscillatory components through a fully data-driven approach, and enables the isolation of intrinsic neurophysiological activation patterns across multiple frequency bands from other interfering components. Unlike other methods, this approach is inherently equipped to handle the multivariate nature of fMRI data by aligning frequency information across multiple regions of interest. The proposed method was validated using three fMRI experiments: resting-state, motor and gambling experiments. Results demonstrate the capability of the methodology to extract reliable and reproducible FC patterns across individuals while uncovering unique connectivity features at different times scales. In addition, the results evidence the effect of the different task on the spectral organization of FC patterns, highlighting the importance of multiscale analysis for understanding functional interactions.
本文探索了一种替代方法，用于使用一类基于自适应频率的方法（称为多变量模式分解（MMD））在多个时间尺度上分析功能磁共振成像（fMRI）数据中的静态功能连接（FC）。所提出的方法通过完全数据驱动的方法将fMRI分解为其固有的多变量振荡成分，并能够从其他干扰成分中分离出多个频带内固有的神经生理激活模式。与其他方法不同，该方法通过对齐多个感兴趣区域的频率信息，天生就具备处理fMRI数据多变量性质的能力。所提出的方法通过三个fMRI实验进行了验证：静息状态、运动和赌博实验。结果表明，该方法能够在个体间提取可靠且可重复的FC模式，同时揭示不同时间尺度上独特的连接特征。此外，结果证明了不同任务对FC模式频谱组织的影响，突出了多尺度分析对于理解功能相互作用的重要性。
