Department of Psychology, Yale University, New Haven, Connecticut, 06510
Department of Psychology, Yale University, New Haven, Connecticut, 06510.
J Neurosci. 2024 Feb 7;44(6):e0735232023. doi: 10.1523/JNEUROSCI.0735-23.2023.
The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9-10 years; = 1,115 individuals; both sexes; 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex-wise) connectivity information and compare the fine-scale connectome with the traditional parcel-wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition.
功能连接组支持大脑在不同空间尺度上的信息传递，从广泛的皮质区域之间的交换到更精细的顶点连接，这些连接是特定信息处理机制的基础。在成年人中，虽然粗尺度和细尺度的功能连接组都可以预测认知，但细尺度可以预测粗尺度功能连接组两倍的方差。然而，过去的全脑关联研究，特别是使用大型发展样本的研究，主要关注粗连接组，以了解认知个体差异的神经基础。我们使用一个大型儿童队列（年龄 9-10 岁；=1115 人；男女均有；女性占 50%，包括 170 对同卵双胞胎和 219 对异卵双胞胎以及 337 名无关个体），研究了在不同空间尺度上计算的静息态功能连接的可靠性、遗传性和与行为的相关性。我们使用连接超对齐来提高对可靠的细尺度（顶点）连接信息的访问，并将细尺度连接组与传统的分区（粗尺度）功能连接组进行比较。虽然细尺度连接组的个体差异比粗尺度连接组更可靠，但它们的遗传性较低。此外，连接组的对齐和尺度会影响其预测行为的能力，一些认知特征可以通过两个连接组尺度同等程度地预测，但其他不太具有遗传性的认知特征可以通过细尺度连接组更好地预测。总的来说，我们的研究结果表明，功能连接组不同尺度上代表的信息处理存在可分离的个体差异，这些差异反过来又对遗传性和认知具有不同的影响。
