Institute of Neurobiology, Universidad Nacional Autónoma de México. Blvd. Juriquilla, 3001, Querétaro, Querétaro, Mexico.
Centro de Investigación en Matemáticas, Valenciana S/N, Guanajuato, Guanajuato, Mexico.
Neuroimage. 2019 Nov 1;201:116013. doi: 10.1016/j.neuroimage.2019.116013. Epub 2019 Jul 19.
Micro-architectural characteristics of white matter can be inferred through analysis of diffusion-weighted magnetic resonance imaging (dMRI). The diffusion-dependent signal can be analyzed through several methods, with the tensor model being the most frequently used due to its straightforward interpretation and low requirements for acquisition parameters. While valuable information can be gained from the tensor-derived metrics in regions of homogeneous tissue organization, this model does not provide reliable microstructural information at crossing fiber regions, which are pervasive throughout human white matter. Several multiple fiber models have been proposed that seem to overcome the limitations of the tensor, with few providing per-bundle dMRI-derived metrics. However, biological interpretations of such metrics are limited by the lack of histological confirmation. To this end, we developed a straightforward biological validation framework. Unilateral retinal ischemia was induced in ten rats, which resulted in axonal (Wallerian) degeneration of the corresponding optic nerve, while the contralateral was left intact; the intact and injured axonal populations meet at the optic chiasm as they cross the midline, generating a fiber crossing region in which each population has different diffusion properties. Five rats served as controls. High-resolution ex vivo dMRI was acquired five weeks after experimental procedures. We correlated and compared histology to per-bundle descriptors derived from three methodologies for dMRI analysis (constrained spherical deconvolution and two multi-tensor representations). We found a tight correlation between axonal density (as evaluated through automatic segmentation of histological sections) with per-bundle apparent fiber density and fractional anisotropy (derived from dMRI). The multi-fiber methods explored were able to correctly identify the damaged fiber populations in a region of fiber crossings (chiasm). Our results provide validation of metrics that bring substantial and clinically useful information about white-matter tissue at crossing fiber regions. Our proposed framework is useful to validate other current and future dMRI methods.
白质的微观结构特征可以通过分析扩散加权磁共振成像(dMRI)来推断。可以通过几种方法分析扩散依赖性信号,由于其直截了当的解释和对采集参数的低要求,张量模型是最常用的方法。虽然在组织均匀的区域,从张量衍生的指标中可以获得有价值的信息,但该模型在纤维交叉区域并不能提供可靠的微观结构信息,而纤维交叉区域在人类白质中普遍存在。已经提出了几种多纤维模型,它们似乎克服了张量的局限性,其中很少有模型提供每束纤维的 dMRI 衍生指标。然而,由于缺乏组织学确认,这些指标的生物学解释受到限制。为此,我们开发了一个简单直接的生物学验证框架。在 10 只大鼠中诱导单侧视网膜缺血,导致相应视神经的轴突(沃勒氏)变性,而对侧保持完整;完整和损伤的轴突群在视交叉处相遇,因为它们穿过中线,在纤维交叉区域产生了每个群都具有不同扩散特性的区域。5 只大鼠作为对照。在实验程序后五周,获取高分辨率离体 dMRI。我们将组织学与三种 dMRI 分析方法(约束球谐分解和两种多张量表示)衍生的每束纤维描述符进行了关联和比较。我们发现,通过对组织学切片进行自动分割评估的轴突密度与每束纤维表观密度和各向异性分数(来自 dMRI)之间存在紧密相关性。在纤维交叉(视交叉)区域,探索的多纤维方法能够正确识别受损的纤维群。我们的结果为在纤维交叉区域提供了有关白质组织的有意义且临床有用的信息的指标提供了验证。我们提出的框架可用于验证其他当前和未来的 dMRI 方法。