Reid Andrew T, Lewis John, Bezgin Gleb, Khundrakpam Budhachandra, Eickhoff Simon B, McIntosh Anthony R, Bellec Pierre, Evans Alan C
Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
Neuroimage. 2016 Jan 15;125:311-331. doi: 10.1016/j.neuroimage.2015.10.057. Epub 2015 Oct 26.
In systems neuroscience, the term "connectivity" has been defined in numerous ways, according to the particular empirical modality from which it is derived. Due to large differences in the phenomena measured by these modalities, the assumptions necessary to make inferences about axonal connections, and the limitations accompanying each, brain connectivity remains an elusive concept. Despite this, only a handful of studies have directly compared connectivity as inferred from multiple modalities, and there remains much ambiguity over what the term is actually referring to as a biological construct. Here, we perform a direct comparison based on the high-resolution and high-contrast Enhanced Nathan Klein Institute (NKI) Rockland Sample neuroimaging data set, and the CoCoMac database of tract tracing studies. We compare four types of commonly-used primate connectivity analyses: tract tracing experiments, compiled in CoCoMac; group-wise correlation of cortical thickness; tractographic networks computed from diffusion-weighted MRI (DWI); and correlational networks obtained from resting-state BOLD (fMRI). We find generally poor correspondence between all four modalities, in terms of correlated edge weights, binarized comparisons of thresholded networks, and clustering patterns. fMRI and DWI had the best agreement, followed by DWI and CoCoMac, while other comparisons showed striking divergence. Networks had the best correspondence for local ipsilateral and homotopic contralateral connections, and the worst correspondence for long-range and heterotopic contralateral connections. k-Means clustering highlighted the lowest cross-modal and cross-species consensus in lateral and medial temporal lobes, anterior cingulate, and the temporoparietal junction. Comparing the NKI results to those of the lower resolution/contrast International Consortium for Brain Imaging (ICBM) dataset, we find that the relative pattern of intermodal relationships is preserved, but the correspondence between human imaging connectomes is substantially better for NKI. These findings caution against using "connectivity" as an umbrella term for results derived from single empirical modalities, and suggest that any interpretation of these results should account for (and ideally help explain) the lack of multimodal correspondence.
在系统神经科学中,“连接性”一词根据其推导所基于的特定实证方式有多种定义。由于这些方式所测量的现象存在很大差异,对轴突连接进行推断所需的假设以及每种方式所伴随的局限性,脑连接性仍然是一个难以捉摸的概念。尽管如此,只有少数研究直接比较了从多种方式推断出的连接性,并且对于该术语实际上作为一种生物学结构所指的内容仍存在很多模糊性。在此,我们基于高分辨率和高对比度的增强型内森·克莱因研究所(NKI)罗克兰样本神经影像数据集以及束路追踪研究的CoCoMac数据库进行了直接比较。我们比较了四种常用的灵长类动物连接性分析:CoCoMac中汇编的束路追踪实验;皮质厚度的组间相关性;从扩散加权磁共振成像(DWI)计算得出的纤维束成像网络;以及从静息态BOLD(功能磁共振成像)获得的相关网络。我们发现,就相关边权重、阈值化网络的二值化比较以及聚类模式而言,所有这四种方式之间的对应关系普遍较差。功能磁共振成像和扩散加权成像的一致性最好,其次是扩散加权成像和CoCoMac,而其他比较则显示出显著差异。网络在局部同侧和同位对侧连接方面的对应关系最好,而在远程和异位对侧连接方面的对应关系最差。k均值聚类突出了外侧和内侧颞叶、前扣带回以及颞顶叶交界处最低的跨模态和跨物种一致性。将NKI的结果与分辨率/对比度较低的国际脑成像联盟(ICBM)数据集的结果进行比较,我们发现多模态关系的相对模式得以保留,但NKI的人类成像连接组之间的对应关系要好得多。这些发现提醒我们不要将“连接性”用作从单一实证方式得出的结果的统称,并表明对这些结果的任何解释都应考虑到(并理想地有助于解释)多模态对应关系的缺乏。