Costantini Isa, Deriche Rachid, Deslauriers-Gauthier Samuel
Inria, Université Côte d'Azur, Valbonne, France.
Front Neuroimaging. 2022 Apr 1;1:815423. doi: 10.3389/fnimg.2022.815423. eCollection 2022.
Functional Magnetic Resonance Imaging (fMRI) is a non-invasive imaging technique that provides an indirect view into brain activity the blood oxygen level dependent (BOLD) response. In particular, resting-state fMRI poses challenges to the recovery of brain activity without prior knowledge on the experimental paradigm, as it is the case for task fMRI. Conventional methods to infer brain activity from the fMRI signals, for example, the general linear model (GLM), require the knowledge of the experimental paradigm to define regressors and estimate the contribution of each voxel's time course to the task. To overcome this limitation, approaches to deconvolve the BOLD response and recover the underlying neural activations without a priori information on the task have been proposed. State-of-the-art techniques, and in particular the total activation (TA), formulate the deconvolution as an optimization problem with decoupled spatial and temporal regularization and an optimization strategy that alternates between the constraints.
In this work, we propose a paradigm-free regularization algorithm named Anisotropic 4D-fMRI (A4D-fMRI) that is applied on the 4D fMRI image, acting simultaneously in the 3D space and 1D time dimensions. Based on the idea that large image variations should be preserved as they occur during brain activations, whereas small variations considered as noise should be removed, the A4D-fMRI applies an anisotropic regularization, thus recovering the location and the duration of brain activations.
Using the experimental paradigm as ground truth, the A4D-fMRI is validated on synthetic and real task-fMRI data from 51 subjects, and its performance is compared to the TA. Results show higher correlations of the recovered time courses with the ground truth compared to the TA and lower computational times. In addition, we show that the A4D-fMRI recovers activity that agrees with the GLM, without requiring or using any knowledge of the experimental paradigm.
功能磁共振成像（fMRI）是一种非侵入性成像技术，它能提供对大脑活动的间接观察——血氧水平依赖（BOLD）反应。特别是，静息态fMRI在没有关于实验范式的先验知识的情况下，对大脑活动的恢复提出了挑战，而任务fMRI则不是这种情况。从fMRI信号推断大脑活动的传统方法，例如一般线性模型（GLM），需要实验范式的知识来定义回归变量并估计每个体素时间历程对任务的贡献。为了克服这一限制，人们提出了在没有任务先验信息的情况下对BOLD反应进行反卷积并恢复潜在神经激活的方法。最先进的技术，特别是全激活（TA），将反卷积表述为一个具有解耦空间和时间正则化的优化问题以及一种在约束之间交替的优化策略。
在这项工作中，我们提出了一种名为各向异性4D-fMRI（A4D-fMRI）的无范式正则化算法，该算法应用于4D fMRI图像，同时在3D空间和1D时间维度上起作用。基于在大脑激活过程中出现的大图像变化应被保留，而被视为噪声的小变化应被去除的想法，A4D-fMRI应用各向异性正则化，从而恢复大脑激活的位置和持续时间。
以实验范式作为基本事实，A4D-fMRI在来自51名受试者的合成和真实任务fMRI数据上得到验证，并将其性能与TA进行比较。结果表明，与TA相比，恢复的时间历程与基本事实的相关性更高，计算时间更短。此外，我们表明A4D-fMRI恢复的活动与GLM一致，而无需或使用任何关于实验范式的知识。
