Brain activation mapping using functional magnetic resonance imaging (fMRI) has been extensively studied in brain gray matter (GM), whereas in large disregarded for probing white matter (WM). This unbalanced treatment has been in part due to controversies in relation to the nature of the blood oxygenation level-dependent (BOLD) contrast in WM and its detachability. However, an accumulating body of studies has provided solid evidence of the functional significance of the BOLD signal in WM and has revealed that it exhibits anisotropic spatio-temporal correlations and structure-specific fluctuations concomitant with those of the cortical BOLD signal. In this work, we present an anisotropic spatial filtering scheme for smoothing fMRI data in WM that accounts for known spatial constraints on the BOLD signal in WM. In particular, the spatial correlation structure of the BOLD signal in WM is highly anisotropic and closely linked to local axonal structure in terms of shape and orientation, suggesting that isotropic Gaussian filters conventionally used for smoothing fMRI data are inadequate for denoising the BOLD signal in WM. The fundamental element in the proposed method is a graph-based description of WM that encodes the underlying anisotropy observed across WM, derived from diffusion-weighted MRI data. Based on this representation, and leveraging graph signal processing principles, we design subject-specific spatial filters that adapt to a subject's unique WM structure at each position in the WM that they are applied at. We use the proposed filters to spatially smooth fMRI data in WM, as an alternative to the conventional practice of using isotropic Gaussian filters. We test the proposed filtering approach on two sets of simulated phantoms, showcasing its greater sensitivity and specificity for the detection of slender anisotropic activations, compared to that achieved with isotropic Gaussian filters. We also present WM activation mapping results on the Human Connectome Project's 100-unrelated subject dataset, across seven functional tasks, showing that the proposed method enables the detection of streamline-like activations within axonal bundles.

中文翻译：

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使用光谱图过滤器在白色物质中对fMRI数据进行扩散告知的空间平滑

使用功能磁共振成像（fMRI）的大脑激活图谱已在脑灰质（GM）中进行了广泛研究，而在很大程度上不考虑白质（WM）的研究。这种不平衡的治疗部分是由于与WM中血液氧合水平依赖性（BOLD）对比的性质及其可分离性有关的争议。然而，大量的研究为WOLD中BOLD信号的功能重要性提供了有力的证据，并揭示了它显示出与皮质BOLD信号相伴的各向异性时空相关性和结构特异性波动。在这项工作中，我们提出了一种各向异性的空间滤波方案，用于平滑WM中的fMRI数据，该方案考虑了WM中BOLD信号的已知空间约束。尤其是，WM中BOLD信号的空间相关结构高度各向异性，并且在形状和方向方面与局部轴突结构紧密相关，这表明传统上用于平滑fMRI数据的各向同性高斯滤波器不足以对WM中的BOLD信号进行去噪。所提出的方法的基本要素是对WM的基于图形的描述，该描述对从WM扩散加权MRI数据得出的WM观察到的基本各向异性进行编码。基于此表示形式，并利用图形信号处理原理，我们设计了特定于对象的空间滤波器，这些滤波器适用于对象在其所应用的WM中的每个位置处的唯一WM结构。我们使用提出的过滤器在WM中对fMRI数据进行空间平滑处理，以替代使用各向同性高斯过滤器的常规做法。我们在两组模拟体模上测试了所提出的滤波方法，显示出与各向同性高斯滤波器相比，它对于检测细长各向异性激活具有更高的灵敏度和特异性。我们还通过七个功能性任务，在人类Connectome项目的100个无关主题数据集上显示了WM激活映射结果，表明所提出的方法能够检测轴突束中类似流线型的激活。