The Mind Research Network, Albuquerque, NM, 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA.
The Mind Research Network, Albuquerque, NM, 87106, USA.
Neuroimage. 2018 Oct 1;179:448-470. doi: 10.1016/j.neuroimage.2018.06.024. Epub 2018 Jun 15.
Independent component analysis (ICA) and seed-based analyses are widely used techniques for studying intrinsic neuronal activity in task-based or resting scans. In this work, we show there is a direct link between the two, and show that there are some important differences between the two approaches in terms of what information they capture. We developed an enhanced connectivity-matrix independent component analysis (cmICA) for calculating whole brain voxel maps of functional connectivity, which reduces the computational complexity of voxel-based connectivity analysis on performing many temporal correlations. We also show there is a mathematical equivalency between parcellations on voxel-to-voxel functional connectivity and simplified cmICA. Next, we used this cost-efficient data-driven method to examine the resting state fMRI connectivity in schizophrenia patients (SZ) and healthy controls (HC) on a whole brain scale and further quantified the relationship between brain functional connectivity and cognitive performances measured by the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) battery. Current results suggest that SZ exhibit a wide-range abnormality, primarily a decrease, in functional connectivity both between networks and within different network hubs. Specific functional connectivity decreases were associated with MATRICS performance deficits. In addition, we found that resting state functional connectivity decreases was extensively associated with aging regardless of groups. In contrast, there was no relationship between positive and negative symptoms in the patients and functional connectivity. In sum, we have developed a novel mathematical relationship between ICA and seed-based connectivity that reduces computational complexity, which has broad applicability, and showed a specific application of this approach to characterize connectivity changes associated with cognitive scores in SZ.
独立成分分析（ICA）和基于种子的分析是用于研究任务或静息扫描中内在神经元活动的广泛使用的技术。在这项工作中，我们展示了两者之间存在直接联系，并表明在它们捕获的信息方面，两种方法之间存在一些重要差异。我们开发了一种增强的连接矩阵独立成分分析（cmICA），用于计算全脑体素功能连接的体素图，这降低了基于体素的连接分析在执行许多时间相关性时的计算复杂度。我们还表明，在体素到体素功能连接和简化的 cmICA 上进行分割之间存在数学等效性。接下来，我们使用这种具有成本效益的数据驱动方法，在全脑范围内检查精神分裂症患者（SZ）和健康对照（HC）的静息状态 fMRI 连接，并进一步量化了大脑功能连接与认知表现之间的关系，认知治疗研究提高精神分裂症（MATRICS）电池。目前的结果表明，SZ 表现出广泛的异常，主要是网络之间和不同网络中心内的功能连接减少。特定的功能连接减少与 MATRICS 表现缺陷有关。此外，我们发现，无论分组如何，静息状态功能连接减少与衰老广泛相关。相比之下，患者的阳性和阴性症状与功能连接之间没有关系。总之，我们已经在 ICA 和基于种子的连接之间建立了一种新的数学关系，该关系降低了计算复杂度，具有广泛的适用性，并展示了这种方法在表征与 SZ 认知评分相关的连接变化方面的特定应用。
