Behavioral self-regulation develops rapidly during childhood and struggles in this area can have lifelong negative outcomes. Challenges with self-regulation are common to several neurodevelopmental conditions, including Autism Spectrum Disorder (ASD). Little is known about the neural expression of behavioral regulation in children with and without neurodevelopmental conditions.

We examined whole-brain brain functional correlations (FC) and behavioral regulation through connectome predictive modelling (CPM). CPM is a data-driven protocol for developing predictive models of brain–behavior relationships and assessing their potential as ‘neuromarkers’ using cross-validation. The data stems from the ABIDE II and comprises 276 children with and without ASD (8–13 years).

We identified networks whose FC predicted individual differences in behavioral regulation. These network models predicted novel individuals’ inhibition and shifting from FC data in both a leave-one-out, and split halves, cross-validation. We observed commonalities and differences, with inhibition relying on more posterior networks, shifting relying on more anterior networks, and both involving regions of the DMN.

Our findings substantially add to our knowledge on the neural expressions of inhibition and shifting across children with and without a neurodevelopmental condition. Given the numerous behavioral issues that can be quantified dimensionally, refinement of whole-brain neuromarker techniques may prove useful in the future.

行为自我调节在儿童时期迅速发展，在这一领域的斗争可能会带来终生的负面结果。自我调节面临的挑战在几种神经发育疾病中很常见，包括自闭症谱系障碍（ASD）。对于有和没有神经发育状况的儿童，行为调节的神经表达知之甚少。

我们通过连接基因组预测模型（CPM）检查了全脑大脑功能相关性（FC）和行为调节。CPM是一种数据驱动的协议，用于开发脑-行为关系的预测模型并使用交叉验证评估其作为“神经标志物”的潜力。数据来自ABIDE II，包括276名有或没有ASD的儿童（8-13岁）。

我们确定了网络，其网络功能预测了行为调节的个体差异。这些网络模型预测了新人的抑制和从FC数据中进行留一法和两半法交叉验证的转变。我们观察到共性和差异，抑制作用依赖于更多的后部网络，转移依赖于更多的前部网络，并且都涉及DMN的区域。

我们的发现大大增加了我们对患有和不患有神经发育疾病的儿童的抑制和转移神经表达的认识。考虑到可以量化地量化的众多行为问题，全脑神经标记技术的完善可能在将来被证明是有用的。