基于连接性的固定点增强:存在交叉纤维时扩散磁共振成像测量的全脑统计分析。

Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres.

作者信息

Raffelt David A, Smith Robert E, Ridgway Gerard R, Tournier J-Donald, Vaughan David N, Rose Stephen, Henderson Robert, Connelly Alan

机构信息

Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia.

Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia.

出版信息

Neuroimage. 2015 Aug 15;117:40-55. doi: 10.1016/j.neuroimage.2015.05.039. Epub 2015 May 22.

Abstract

In brain regions containing crossing fibre bundles, voxel-average diffusion MRI measures such as fractional anisotropy (FA) are difficult to interpret, and lack within-voxel single fibre population specificity. Recent work has focused on the development of more interpretable quantitative measures that can be associated with a specific fibre population within a voxel containing crossing fibres (herein we use fixel to refer to a specific fibre population within a single voxel). Unfortunately, traditional 3D methods for smoothing and cluster-based statistical inference cannot be used for voxel-based analysis of these measures, since the local neighbourhood for smoothing and cluster formation can be ambiguous when adjacent voxels may have different numbers of fixels, or ill-defined when they belong to different tracts. Here we introduce a novel statistical method to perform whole-brain fixel-based analysis called connectivity-based fixel enhancement (CFE). CFE uses probabilistic tractography to identify structurally connected fixels that are likely to share underlying anatomy and pathology. Probabilistic connectivity information is then used for tract-specific smoothing (prior to the statistical analysis) and enhancement of the statistical map (using a threshold-free cluster enhancement-like approach). To investigate the characteristics of the CFE method, we assessed sensitivity and specificity using a large number of combinations of CFE enhancement parameters and smoothing extents, using simulated pathology generated with a range of test-statistic signal-to-noise ratios in five different white matter regions (chosen to cover a broad range of fibre bundle features). The results suggest that CFE input parameters are relatively insensitive to the characteristics of the simulated pathology. We therefore recommend a single set of CFE parameters that should give near optimal results in future studies where the group effect is unknown. We then demonstrate the proposed method by comparing apparent fibre density between motor neurone disease (MND) patients with control subjects. The MND results illustrate the benefit of fixel-specific statistical inference in white matter regions that contain crossing fibres.

摘要

在包含交叉纤维束的脑区,诸如分数各向异性(FA)等体素平均扩散磁共振成像测量结果难以解释,且缺乏体素内单纤维群体特异性。近期的研究工作聚焦于开发更具可解释性的定量测量方法,这些方法能够与包含交叉纤维的体素内特定纤维群体相关联(在此我们使用固定像素来指代单个体素内的特定纤维群体)。不幸的是,传统的用于平滑处理和基于聚类的统计推断的三维方法不能用于这些测量的基于体素的分析,因为当相邻体素可能具有不同数量的固定像素时,用于平滑处理和聚类形成的局部邻域可能不明确,或者当它们属于不同束时定义不清晰。在此,我们引入一种新的统计方法来进行基于固定像素的全脑分析,称为基于连通性的固定像素增强(CFE)。CFE使用概率纤维束成像来识别可能共享潜在解剖结构和病理特征的结构连通固定像素。然后,概率连通性信息用于特定束的平滑处理(在统计分析之前)以及统计图谱的增强(使用类似无阈值聚类增强的方法)。为了研究CFE方法的特性,我们使用在五个不同白质区域(选择以涵盖广泛的纤维束特征)中一系列测试统计信噪比生成的模拟病理,通过大量CFE增强参数和平滑程度的组合来评估敏感性和特异性。结果表明,CFE输入参数对模拟病理的特征相对不敏感。因此,我们推荐一组单一的CFE参数,在未来未知组效应的研究中应能给出接近最优的结果。然后,我们通过比较运动神经元病(MND)患者与对照受试者之间的表观纤维密度来展示所提出的方法。MND的结果说明了在包含交叉纤维的白质区域中基于固定像素的统计推断的优势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/92ff/4528070/203992600d77/fx1.jpg

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