The impact of traditional neuroimaging methods on the spatial localization of cortical areas
The impact of traditional neuroimaging methods on the spatial localization of cortical areas
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DOI：
10.1073/pnas.1801582115
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发表时间：
2018-07-03
影响因子：
11.1
通讯作者：
Glasser, Matthew F.
中科院分区：
文献类型：
--
作者：
Coalson, Timothy S.;Van Essen, David C.;Glasser, Matthew F.
定位人类大脑功能是系统神经科学的一个长期目标。为了实现这一目标，神经成像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，并报告相对于基于体积的分组的结果。最近，利用来自人类连接组项目的多模态数据生成了一种新的360区域的基于表面的皮层分割，这种基于体积的分割经常被要求与传统的基于体积的分析一起使用。然而，考虑到传统体积法和人类连接组项目式处理之间的主要方法差异，必须首先确定这种改变的分割方法的实用性和可解释性。从自动生成的个体-受试者分割开始，用不同的方法处理它们，我们发现传统的处理步骤，特别是基于体积的平滑和配准，与基于表面的方法相比，大大降低了皮质区域的定位。我们还表明，基于表面的配准使用与皮质区域紧密相关的特征，而不是单独的折叠模式，可以改善区域的对齐，并且传统的基于体积的方法在很大程度上没有利用高分辨率采集的好处。定量地，我们表明传统方法的最常见版本的空间定位仅为最佳基于表面的方法的35%，使用两个客观测量（最大概率图的峰值面积概率和“捕获面积分数”）进行评估。最后，我们证明，当试图在表面上准确地表示基于体积的群体分析结果时，存在实质性的挑战，这对使用这些基于体积的方法的过去和未来的研究的可解释性具有重要意义。
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.