Group-ICA model order highlights patterns of functional brain connectivity
Group-ICA model order highlights patterns of functional brain connectivity
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DOI：
10.3389/fnsys.2011.00037
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发表时间：
2011-01-01
影响因子：
3
通讯作者：
Kiviniemi, Vesa
中科院分区：
文献类型：
--
作者：
Abou Elseoud, Ahmed;Littow, Hard;Kiviniemi, Vesa
静息状态网络（RSNs）可以使用独立成分分析（ICA）在个体和群体水平上可靠和可重复地检测。改变ICA维数（模型阶数）估计会对rsn的空间特征及其在子网络中的划分产生显著影响。最近来自几项神经影像学研究的证据表明，人类大脑有一个模块化的层次组织，类似于不同的ICA模型顺序所描述的层次结构。我们假设用ICA测量的组间功能连通性差异可能受到模型顺序选择的影响。我们使用所谓的双重回归作为ICA模型顺序的函数，研究了一组未用药的季节性情感障碍（SAD）患者与正常健康对照者在功能连通性方面的差异。结果表明，检测到的疾病相关的功能连接差异作为ICA模型顺序的函数而改变。组间差异体积随ICA模型阶数的变化有显著变化，在模型阶数70（似乎是表达组间差异最大的最优点）时达到最大值，之后趋于稳定。我们的研究结果表明，细粒度的rsn能够更好地检测详细的疾病相关功能连接变化。然而，高模型订单显示需要克服的误报风险增加。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。
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.