标题： 大脑网络刚性维度的细微变化导致了认知能力的个体差异 显示英文标题

标题： Subtle variations in stiff dimensions of brain networks account for individual differences in cognitive ability

摘要： 解释认知能力的个体差异需要既识别出跨个体变化的大脑参数，又理解大脑网络如何被招募来完成特定任务。 通常，任务表现依赖于功能亚网络的整合与分离，这些常通过区域兴奋性和连接性等参数来捕捉。 然而，这些参数的高维特性阻碍了确定它们的功能相关性。 在这里，我们对人类大脑数据应用刚性-松弛分析，揭示某些微妙的参数组合（“刚性维度”）在任务处理过程中强有力地影响神经活动，而另一些（“松弛维度”）则变化更大但影响微乎其微。 利用基于任务fMRI的二元最大熵模型，我们表明即使通过Fisher信息矩阵分析得出的刚性维度的小偏差也支配默认模式网络（DMN）和工作记忆网络（WMN）之间分离与整合之间的动态相互作用。 至关重要的是，区分0-back任务（警觉注意力）和2-back任务（工作记忆更新）揭示了预测每种条件性能的部分不同的刚性维度，以及两项任务共享的整体DMN-WMN分离。 总之，刚性-松弛分析挑战了对大参数变异性的传统关注，强调了这些微妙却功能上决定性的参数组合。 显示英文摘要

摘要： Explaining individual differences in cognitive abilities requires both identifying brain parameters that vary across individuals and understanding how brain networks are recruited for specific tasks. Typically, task performance relies on the integration and segregation of functional subnetworks, often captured by parameters like regional excitability and connectivity. Yet, the high dimensionality of these parameters hinders pinpointing their functional relevance. Here, we apply stiff-sloppy analysis to human brain data, revealing that certain subtle parameter combinations ("stiff dimensions") powerfully influence neural activity during task processing, whereas others ("sloppy dimensions") vary more extensively but exert minimal impact. Using a pairwise maximum entropy model of task fMRI, we show that even small deviations in stiff dimensions-derived through Fisher Information Matrix analysis-govern the dynamic interplay of segregation and integration between the default mode network (DMN) and a working memory network (WMN). Crucially, separating a 0-back task (vigilant attention) from a 2-back task (working memory updating) uncovers partially distinct stiff dimensions predicting performance in each condition, along with a global DMN-WMN segregation shared across both tasks. Altogether, stiff-sloppy analysis challenges the conventional focus on large parameter variability by highlighting these subtle yet functionally decisive parameter combinations.