School of Mathematical Sciences, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China.
Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China.
Neuroimage. 2024 Aug 1;296:120657. doi: 10.1016/j.neuroimage.2024.120657. Epub 2024 May 27.
The complexity of fMRI signals quantifies temporal dynamics of spontaneous neural activity, which has been increasingly recognized as providing important insights into cognitive functions and psychiatric disorders. However, its heritability and structural underpinnings are not well understood. Here, we utilize multi-scale sample entropy to extract resting-state fMRI complexity in a large healthy adult sample from the Human Connectome Project. We show that fMRI complexity at multiple time scales is heritable in broad brain regions. Heritability estimates are modest and regionally variable. We relate fMRI complexity to brain structure including surface area, cortical myelination, cortical thickness, subcortical volumes, and total brain volume. We find that surface area is negatively correlated with fine-scale complexity and positively correlated with coarse-scale complexity in most cortical regions, especially the association cortex. Most of these correlations are related to common genetic and environmental effects. We also find positive correlations between cortical myelination and fMRI complexity at fine scales and negative correlations at coarse scales in the prefrontal cortex, lateral temporal lobe, precuneus, lateral parietal cortex, and cingulate cortex, with these correlations mainly attributed to common environmental effects. We detect few significant associations between fMRI complexity and cortical thickness. Despite the non-significant association with total brain volume, fMRI complexity exhibits significant correlations with subcortical volumes in the hippocampus, cerebellum, putamen, and pallidum at certain scales. Collectively, our work establishes the genetic basis and structural correlates of resting-state fMRI complexity across multiple scales, supporting its potential application as an endophenotype for psychiatric disorders.
功能磁共振成像（fMRI）信号的复杂性量化了自发神经活动的时间动态，这已越来越被认为可以为认知功能和精神障碍提供重要的见解。然而，其遗传性和结构基础尚不清楚。在这里，我们利用多尺度样本熵从人类连接组计划的大型健康成年人样本中提取静息状态 fMRI 复杂性。我们表明，在广泛的大脑区域中，多个时间尺度的 fMRI 复杂性是可遗传的。遗传率估计值适中且具有区域变异性。我们将 fMRI 复杂性与脑结构相关联，包括表面积、皮质髓鞘化、皮质厚度、皮质下体积和全脑体积。我们发现，在大多数皮质区域（尤其是联合皮质）中，表面积与细粒度复杂性呈负相关，与粗粒度复杂性呈正相关。这些相关性中的大多数与常见的遗传和环境因素有关。我们还发现，在额皮质、外侧颞叶、楔前叶、外侧顶叶和扣带皮质中，皮质髓鞘化与 fMRI 复杂性在细粒度上呈正相关，在粗粒度上呈负相关，这些相关性主要归因于共同的环境因素。我们在 fMRI 复杂性与皮质厚度之间检测到很少的显著相关性。尽管与全脑体积无显著相关性，但 fMRI 复杂性在某些尺度上与海马体、小脑、壳核和苍白球的皮质下体积呈显著相关。总的来说，我们的工作确立了静息状态 fMRI 复杂性在多个尺度上的遗传基础和结构相关性，支持其作为精神障碍的潜在表型的应用。
