Finding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent sources of information that link both modalities at the voxel level. We performed separate analyses on sets of projections from the visual cortex, the caudoputamen and the midbrain reticular nucleus, and we determined those brain areas, injections and genes that were most involved in independent components that link both gene expression and projection density data, while we validated their biological context through enrichment analysis. We identified representative and literature-validated cortico-midbrain and cortico-striatal projections, whose gene subsets were enriched with annotations for neuronal and synaptic function and related developmental and metabolic processes. The results were highly reproducible when including all available projections, as well as consistent with factorisations obtained using the Dictionary Learning and Sparse Coding technique. Hence, Linked ICA yielded reproducible independent components that were preserved under increasing data variance. Taken together, we have developed and validated a novel paradigm for linking gene expression and structural projection patterns in the mouse mesoconnectome, which can power future studies aiming to relate genes to brain function.

寻找基因和结构连接之间的联系对于解开大脑连接组的潜在机制至关重要。在这项研究中，我们通过将 Linked ICA 方法的修改版本应用于艾伦脑科学研究所的体积数据来识别连接这两种模式的独立信息来源，从而确定了小鼠大脑中基因表达和轴突投影密度之间的联系在体素级别。我们对视觉皮层、尾壳和中脑网状核的投射集进行了单独分析，我们确定了那些最参与连接基因表达和投射密度数据的独立成分的大脑区域、注射和基因，而我们通过富集分析验证了它们的生物学背景。我们确定了具有代表性和文献验证的皮质-中脑和皮质-纹状体投射，其基因子集富含神经元和突触功能以及相关发育和代谢过程的注释。当包括所有可用的投影时，结果具有高度可重复性，并且与使用字典学习和稀疏编码技术获得的因式分解一致。因此，链接 ICA 产生了可重复的独立分量，这些分量在数据方差增加的情况下得以保留。总之，我们已经开发并验证了一种新的范例，用于将小鼠中连接组中的基因表达和结构投影模式联系起来，这可以为未来旨在将基因与大脑功能联系起来的研究提供动力。