Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110.
Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110;
Proc Natl Acad Sci U S A. 2018 Jul 3;115(27):E6356-E6365. doi: 10.1073/pnas.1801582115. Epub 2018 Jun 20.
Localizing human brain functions is a long-standing goal in systems neuroscience. Toward this goal, neuroimaging studies have traditionally used volume-based smoothing, registered data to volume-based standard spaces, and reported results relative to volume-based parcellations. A novel 360-area surface-based cortical parcellation was recently generated using multimodal data from the Human Connectome Project, and a volume-based version of this parcellation has frequently been requested for use with traditional volume-based analyses. However, given the major methodological differences between traditional volumetric and Human Connectome Project-style processing, the utility and interpretability of such an altered parcellation must first be established. By starting from automatically generated individual-subject parcellations and processing them with different methodological approaches, we show that traditional processing steps, especially volume-based smoothing and registration, substantially degrade cortical area localization compared with surface-based approaches. We also show that surface-based registration using features closely tied to cortical areas, rather than to folding patterns alone, improves the alignment of areas, and that the benefits of high-resolution acquisitions are largely unexploited by traditional volume-based methods. Quantitatively, we show that the most common version of the traditional approach has spatial localization that is only 35% as good as the best surface-based method as assessed using two objective measures (peak areal probabilities and "captured area fraction" for maximum probability maps). Finally, we demonstrate that substantial challenges exist when attempting to accurately represent volume-based group analysis results on the surface, which has important implications for the interpretability of studies, both past and future, that use these volume-based methods.
本地化人类大脑功能是系统神经科学的长期目标。为此，神经影像学研究传统上使用基于体积的平滑、将数据注册到基于体积的标准空间，并根据基于体积的分割报告结果。最近，使用来自人类连接组计划的多模态数据生成了一种新颖的 360 个区域的基于表面的皮质分割，并且经常需要基于体积的版本与传统的基于体积的分析一起使用。然而，鉴于传统体积和人类连接组计划式处理之间存在重大的方法学差异，这种改变的分割的实用性和可解释性必须首先建立。通过从自动生成的个体分割开始，并使用不同的方法处理它们，我们表明，与基于表面的方法相比，传统的处理步骤，特别是基于体积的平滑和注册，会大大降低皮质区域的定位。我们还表明，使用与皮质区域紧密相关的特征而不是仅与折叠模式相关的特征进行基于表面的注册，可以改善区域的对齐，并且传统基于体积的方法在很大程度上没有利用高分辨率采集的优势。定量地，我们表明，最常见的传统方法版本的空间定位仅为最好的基于表面方法的 35%，这是使用两种客观测量方法（最大概率图的峰值面积概率和“捕获面积分数”）评估的。最后，我们证明当试图在表面上准确表示基于体积的组分析结果时存在重大挑战，这对过去和未来使用这些基于体积的方法的研究的可解释性具有重要意义。
