Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1867-1870. doi: 10.1109/EMBC48229.2022.9871305.
Resting-state functional network connectivity (rsFNC) has shown utility for identifying characteristic functional brain patterns in individuals with psychiatric and mood disorders, providing a promising avenue for biomarker development. However, several factors have precluded widespread clinical adoption of rsFNC diagnostics, namely the lack of standardized approaches for capturing comparable and reproducible imaging markers across individuals, as well as the disagreement on the amount of data required to robustly detect intrinsic connectivity networks (ICNs) and diagnostically relevant patterns of rsFNC. Here, we investigate the robustness of (1) subject-specific ICNs standardized to an a priori network template via spatially constrained ICA (scICA), and (2) rsFNC differences between schizophrenia and control groups with respect to the length of the fMRI. Our results suggest clinical rsFMRI scans, when decomposed with scICA, could potentially be shortened to just 2-4 minutes without significant loss of individual rsFNC information or classification performance of longer scan lengths. Clinical Relevance - This work shows diagnostically relevant rsFNC patterns for schizophrenia can be identified from just 2-4 minutes of rsfMRI using an scICA approach. These results can influence future work in neuroimaging biomarker development.
静息态功能网络连接（rsFNC）已被证明可用于识别精神和情绪障碍个体的特征性功能脑模式，为生物标志物的开发提供了有希望的途径。然而，有几个因素阻碍了 rsFNC 诊断的广泛临床应用，即缺乏在个体之间捕获可比且可重复的成像标记的标准化方法，以及对检测内在连通性网络（ICN）和 rsFNC 的诊断相关模式所需的数据量存在分歧。在这里，我们研究了以下方面的稳健性：（1）通过空间约束 ICA（scICA）标准化到先验网络模板的特定于个体的 ICN；（2） 对于 fMRI 的长度，精神分裂症组和对照组之间的 rsFNC 差异。我们的结果表明，当使用 scICA 分解时，临床 rsFMRI 扫描可能会缩短到仅 2-4 分钟，而不会显著损失个体 rsFNC 信息或较长扫描长度的分类性能。临床相关性 - 这项工作表明，使用 scICA 方法，可以从仅 2-4 分钟的 rsfMRI 中识别出与精神分裂症相关的 rsFNC 模式。这些结果可以影响神经影像学生物标志物开发的未来工作。
