Table_1_Stable Meta-Networks, Noise, and Artifacts in the Human Connectome: Low- to High-Dimensional Independent Components Analysis as a Hierarchy of Intrinsic Connectivity Networks.CSV

Connectivity within the human connectome occurs between multiple neuronal systems—at small to very large spatial scales. Independent component analysis (ICA) is potentially a powerful tool to facilitate multi-scale analyses. However, ICA has yet to be fully evaluated at very low (10 or fewer) and ultra-high dimensionalities (200 or greater). The current investigation used data from the Human Connectome Project (HCP) to determine the following: (1) if larger networks, or meta-networks, are present at low dimensionality, (2) if nuisance sources increase with dimensionality, and (3) if ICA is prone to overfitting. Using bootstrap ICA, results suggested that, at very low dimensionality, ICA spatial maps consisted of Visual/Attention and Default/Control meta-networks. At fewer than 10 components, well-known networks such as the Somatomotor Network were absent from results. At high dimensionality, nuisance sources were present even in denoised high-quality data but were identifiable by correlation with tissue probability maps. Artifactual overfitting occurred to a minor degree at high dimensionalities. Basic summary statistics on spatial maps (maximum cluster size, maximum component weight, and average weight outside of maximum cluster) quickly and easily separated artifacts from gray matter sources. Lastly, by using weighted averages of bootstrap stability, even ultra-high dimensional ICA resulted in highly reproducible spatial maps. These results demonstrate how ICA can be applied in multi-scale analyses, reliably and accurately reproducing the hierarchy of meta-networks, large-scale networks, and subnetworks, thereby characterizing cortical connectivity across multiple spatial scales.

人脑连接组内部的连接发生于多个神经元系统之间，其空间尺度从小至极大不等。独立成分分析（ICA）作为一种可能具有强大功效的工具，能够促进多尺度分析。然而，ICA在极低维度（10或以下）和超高维度（200或以上）方面的全面评估尚待进行。本研究利用人类连接组项目（HCP）的数据，旨在确定以下内容：（1）在低维度是否存在较大规模的网络或元网络，（2）是否随着维度的增加，干扰源也随之增多，（3）ICA是否易于过度拟合。通过使用自举ICA，研究结果提示，在极低维度下，ICA的空间图谱主要由视觉/注意力与默认/控制元网络组成。在10个以下成分的情况下，诸如躯体运动网络等知名网络并未出现在结果中。在高维度下，即使在去噪的高质量数据中，干扰源也存在，但可以通过与组织概率图谱的相关性进行识别。在高维度下，人工过度拟合仅发生在一个较小的程度上。通过对空间图谱的基本总结统计（最大簇群大小、最大成分权重以及最大簇群外的平均权重）进行快速且简便的分析，即可将伪影与灰质来源区分开来。最后，通过使用自举稳定性的加权平均值，即使是超高维度的ICA也能够产生高度可重复的空间图谱。这些结果展示了ICA在多尺度分析中的应用，能够可靠且精确地再现元网络、大规模网络和子网络的层次结构，从而描绘出跨越多个空间尺度的皮层连接特征。