Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
Department of Computer Science, Georgia State University, Atlanta, Georgia, USA.
Hum Brain Mapp. 2023 Dec 1;44(17):5729-5748. doi: 10.1002/hbm.26472. Epub 2023 Oct 3.
Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-scale ICNs require less data to achieve specific levels of (within- and between-subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject-level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within-subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.
尽管数据驱动方法具有已知的优势，但缺乏识别能够捕捉个体变异和个体间一致性的功能神经影像学模式的方法，限制了 rsfMRI 的临床应用及其在单个体分析中的应用。在这里，我们使用来自私人和公共数据集的超过 10 万个体的 rsfMRI 数据，通过使用多模型阶独立成分分析（ICA）来识别可复制的多空间尺度的内源性连通网络（ICN）模板。我们还研究了通过空间约束 ICA 来估计个体特异性 ICN 的可行性。结果表明，个体水平的 ICN 估计值随 ICN 本身、数据长度和空间分辨率的变化而变化。一般来说，大规模的 ICN 需要较少的数据就能达到与其模板在个体内和个体间的特定空间相似性水平。重要的是，增加数据长度可以降低 ICN 的个体特异性，这表明更长的扫描时间可能并不总是理想的。我们还发现数据长度和空间平滑度之间存在正线性关系（可能是由于对内在动力学进行平均），这表明研究优化数据长度时应考虑空间平滑度。最后，在不同数据长度下使用完整数据和子集估计的 ICN 之间的空间相似性的一致性表明，较短数据中的个体内空间相似性较低并非完全由 ICN 估计的可靠性较低所致，而是可能表明随着数据长度的增加而平均化的有意义的大脑动力学。
