Department of Diagnostic Radiology, Oulu University Hospital Oulu, Finland.
Front Syst Neurosci. 2011 Jun 3;5:37. doi: 10.3389/fnsys.2011.00037. eCollection 2011.
Resting-state networks (RSNs) can be reliably and reproducibly detected using independent component analysis (ICA) at both individual subject and group levels. Altering ICA dimensionality (model order) estimation can have a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Recent evidence from several neuroimaging studies suggests that the human brain has a modular hierarchical organization which resembles the hierarchy depicted by different ICA model orders. We hypothesized that functional connectivity between-group differences measured with ICA might be affected by model order selection. We investigated differences in functional connectivity using so-called dual regression as a function of ICA model order in a group of unmedicated seasonal affective disorder (SAD) patients compared to normal healthy controls. The results showed that the detected disease-related differences in functional connectivity alter as a function of ICA model order. The volume of between-group differences altered significantly as a function of ICA model order reaching maximum at model order 70 (which seems to be an optimal point that conveys the largest between-group difference) then stabilized afterwards. Our results show that fine-grained RSNs enable better detection of detailed disease-related functional connectivity changes. However, high model orders show an increased risk of false positives that needs to be overcome. Our findings suggest that multilevel ICA exploration of functional connectivity enables optimization of sensitivity to brain disorders.
静息态网络（RSN）可以通过独立成分分析（ICA）在个体和组水平上可靠且可重复地检测到。改变 ICA 维度（模型阶数）估计会对 RSN 的空间特征及其划分为子网络产生重大影响。来自几项神经影像学研究的最新证据表明，人类大脑具有模块化的层次组织，类似于不同 ICA 模型阶数所描绘的层次结构。我们假设，使用 ICA 测量的组间功能连接差异可能会受到模型阶数选择的影响。我们通过所谓的双回归，研究了在一组未经药物治疗的季节性情感障碍（SAD）患者与正常健康对照组中，随着 ICA 模型阶数的变化，功能连接的差异。结果表明，随着 ICA 模型阶数的变化，检测到的功能连接相关疾病差异也发生了变化。组间差异的体积随着 ICA 模型阶数的变化而显著变化，在模型阶数 70 时达到最大值（似乎是一个最佳点，可以传达最大的组间差异），然后稳定下来。我们的结果表明，细粒度的 RSN 可以更好地检测到详细的与疾病相关的功能连接变化。然而，高模型阶数显示出假阳性的风险增加，需要克服。我们的研究结果表明，功能连接的多层次 ICA 探索可以优化对大脑疾病的敏感性。
