Du Jingnan, Elliott Maxwell L, Ladopoulou Joanna, Eldaief Mark C, Buckner Randy L
Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.
Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA.
bioRxiv. 2025 Feb 25:2025.02.25.640090. doi: 10.1101/2025.02.25.640090.
Precision mapping of brain networks within individuals has become a widely used tool that prevailingly relies on functional connectivity analysis of resting-state data. Here we explored whether networks could be precisely estimated solely using data acquired during active task paradigms. The straightforward strategy involved extracting residualized data after application of a task-based general linear model (GLM) and then applying standard functional connectivity analysis. Functional correlation matrices estimated from task data were highly similar to those derived from traditional resting-state fixation data. The largest factor affecting similarity between correlation matrices was the amount of data. Networks estimated within-individual from task data displayed strong spatial overlap with those estimated from resting-state fixation data and predicted the same triple functional dissociation in independent data. The implications of these findings are that (1) existing task data can be reanalyzed to estimate within-individual network organization, (2) resting-state fixation and task data can be pooled to increase statistical power, and (3) future studies can exclusively acquire task data to both estimate networks and extract task responses. Most broadly, the present results suggest that there is an underlying, stable network architecture that is idiosyncratic to the individual and persists across task states.
个体脑网络的精确映射已成为一种广泛使用的工具，该工具主要依赖于静息态数据的功能连接分析。在这里，我们探讨了是否仅使用主动任务范式期间获取的数据就能精确估计网络。直接的策略是在应用基于任务的通用线性模型（GLM）后提取残差化数据，然后应用标准的功能连接分析。从任务数据估计的功能相关矩阵与从传统静息态注视数据导出的矩阵高度相似。影响相关矩阵之间相似性的最大因素是数据量。从任务数据中个体内估计的网络与从静息态注视数据中估计的网络显示出强烈的空间重叠，并在独立数据中预测了相同的三重功能解离。这些发现的意义在于：（1）可以重新分析现有任务数据以估计个体内网络组织；（2）静息态注视数据和任务数据可以合并以提高统计功效；（3）未来的研究可以专门获取任务数据以估计网络并提取任务反应。最广泛地说，目前的结果表明存在一种潜在的、稳定的网络架构，它是个体特有的并且在不同任务状态下持续存在。
