Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Helsinki, Finland.
Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland.
Hum Brain Mapp. 2019 Apr 1;40(5):1391-1402. doi: 10.1002/hbm.24454. Epub 2019 Jan 1.
Brain structure and many brain functions are known to be genetically controlled, but direct links between neuroimaging measures and their underlying cellular-level determinants remain largely undiscovered. Here, we adopt a novel computational method for examining potential similarities in high-dimensional brain imaging data between siblings. We examine oscillatory brain activity measured with magnetoencephalography (MEG) in 201 healthy siblings and apply Bayesian reduced-rank regression to extract a low-dimensional representation of familial features in the participants' spectral power structure. Our results show that the structure of the overall spectral power at 1-90 Hz is a highly conspicuous feature that not only relates siblings to each other but also has very high consistency within participants' own data, irrespective of the exact experimental state of the participant. The analysis is extended by seeking genetic associations for low-dimensional descriptions of the oscillatory brain activity. The observed variability in the MEG spectral power structure was associated with SDK1 (sidekick cell adhesion molecule 1) and suggestively with several other genes that function, for example, in brain development. The current results highlight the potential of sophisticated computational methods in combining molecular and neuroimaging levels for exploring brain functions, even for high-dimensional data limited to a few hundred participants.
大脑结构和许多大脑功能已知受遗传控制，但神经影像学测量与其潜在的细胞水平决定因素之间的直接联系在很大程度上仍未被发现。在这里，我们采用了一种新的计算方法来检查兄弟姐妹之间高维脑成像数据之间的潜在相似性。我们检查了 201 名健康兄弟姐妹的脑磁图（MEG）测量的脑振荡活动，并应用贝叶斯降秩回归来提取参与者光谱功率结构中家族特征的低维表示。我们的结果表明，1-90Hz 范围内的整体光谱功率结构是一个非常明显的特征，它不仅将兄弟姐妹彼此联系起来，而且在参与者自己的数据中也具有非常高的一致性，而与参与者的确切实验状态无关。该分析通过为振荡脑活动的低维描述寻求遗传关联来扩展。观察到的 MEG 光谱功率结构的可变性与 SDK1（侧伴细胞黏附分子 1）相关，并且提示与其他几个基因相关，这些基因例如在大脑发育中起作用。目前的结果强调了复杂的计算方法在结合分子和神经影像学水平探索大脑功能方面的潜力，即使对于受限于几百名参与者的高维数据也是如此。
