Chang Kelly, Burke Luke, LaPiana Nina, Howlett Bradley, Hunt David, Dezelar Margaret, Andre Jalal B, Curl Patti, Ralston James D, Rokem Ariel, Mac Donald Christine L
Department of Psychology, University of Washington.
Kaiser Permanente Washington Health Research Institute.
bioRxiv. 2024 Nov 11:2024.11.10.622861. doi: 10.1101/2024.11.10.622861.
Tractometry of diffusion-weighted magnetic resonance imaging (dMRI) non-invasively quantifies tissue properties of brain connections. It is widely used in aging studies but could be less reliable in aging brains due to increased white matter free water. We demonstrate that computational free water elimination (FWE) increases reliability and accuracy of tractometry in a large (n = 339) cohort of older adults (66 - 103 y.o.). We found substantial (up to ~37%) improvements in reliability in a split-half comparison at every stage of the pipeline: estimation of voxel-level fiber orientation distribution functions, delineation of major pathway trajectories, and assessment of tissue properties along the pathways. FWE also improves inferences from tractometry, producing more accurate cross-validated predictions of clinician Fazekas scores. By sub-sampling a multi-b-value dataset, we demonstrated that these findings generalize to both single-b-value data, which is important for many datasets where only one b-value may be available. Overall, the results highlight the importance of accounting for free water in tractometry studies, especially in aging brains. We provide open-source software for free-water elimination that can be applied to a wide range of clinical and research datasets (https://github.com/nrdg/fwe).
扩散加权磁共振成像（dMRI）纤维束成像可无创地量化脑连接的组织特性。它在衰老研究中被广泛应用，但由于白质自由水增加，在衰老大脑中可能不太可靠。我们证明，在一个由339名老年人（66 - 103岁）组成的大型队列中，计算性自由水消除（FWE）提高了纤维束成像的可靠性和准确性。我们发现在流程的每个阶段进行的对半比较中，可靠性都有显著提高（高达约37%）：体素级纤维方向分布函数的估计、主要通路轨迹的描绘以及沿通路的组织特性评估。FWE还改善了纤维束成像的推断，对临床医生Fazekas评分产生了更准确的交叉验证预测。通过对多b值数据集进行子采样，我们证明这些发现适用于单b值数据，这对于许多可能只有一个b值的数据集很重要。总体而言，结果突出了在纤维束成像研究中考虑自由水的重要性，尤其是在衰老大脑中。我们提供了用于自由水消除的开源软件，可应用于广泛的临床和研究数据集（https://github.com/nrdg/fwe）。
