UCSF-UC Berkeley Graduate Group in Bioengineering, San Francisco, CA.
Department of Neurology, University of California, San Francisco, CA.
J Neuroimaging. 2020 Jul;30(4):443-457. doi: 10.1111/jon.12713. Epub 2020 May 20.
Neurosurgical resection is one of the few opportunities researchers have to image the human brain pre- and postfocal damage. A major challenge associated with brains undergoing surgical resection is that they often do not fit brain templates most image-processing methodologies are based on. Manual intervention is required to reconcile the pathology, requiring time investment and introducing reproducibility concerns, and extreme cases must be excluded.
We propose an automatic longitudinal pipeline based on High Angular Resolution Diffusion Imaging acquisitions to facilitate a Pathway Lesion Symptom Mapping analysis relating focal white matter injury to functional deficits. This two-part approach includes (i) automatic segmentation of focal white matter injury from anisotropic power differences, and (ii) modeling disconnection using tractography on the single-subject level, which specifically identifies the disconnections associated with focal white matter damage.
The advantages of this approach stem from (1) objective and automatic lesion segmentation and tractogram generation, (2) objective and precise segmentation of affected tissue likely to be associated with damage to long-range white matter pathways (defined by anisotropic power), (3) good performance even in the cases of anatomical distortions by use of nonlinear tensor-based registration, which aligns images using an approach sensitive to white matter microstructure.
Mapping a system as variable and complex as the human brain requires sample sizes much larger than the current technology can support. This pipeline can be used to execute large-scale, sufficiently powered analyses by meeting the need for an automatic approach to objectively quantify white matter disconnection.
神经外科切除术是研究人员为数不多的能够在病灶前和病灶后对人脑进行成像的机会之一。与接受手术切除的大脑相关的一个主要挑战是，它们通常与大多数图像处理方法所基于的大脑模板不匹配。需要手动干预来协调病理学，这需要时间投入并引入可重复性问题，并且必须排除极端情况。
我们提出了一种基于高角度分辨率扩散成像采集的自动纵向管道，以促进与病灶白质损伤相关的功能缺陷的路径病变症状映射分析。这种两部分方法包括 (i) 基于各向异性功率差异的病灶白质损伤的自动分割，以及 (ii) 在单个受试者水平上使用轨迹追踪进行的连接中断建模，该方法专门识别与病灶白质损伤相关的连接中断。
这种方法的优点源于 (1) 病灶分割和轨迹追踪生成的客观和自动，(2) 可能与长程白质通路损伤相关的受影响组织的客观和精确分割（由各向异性功率定义），(3) 即使在使用基于非线性张量的配准进行解剖变形的情况下也具有良好的性能，该配准使用对白质微观结构敏感的方法对齐图像。
映射像人脑这样多变和复杂的系统需要比当前技术能够支持的更大的样本量。该管道可以通过满足对客观量化白质连接中断的自动方法的需求来执行大规模、足够有力的分析。
