Imaging methods that span both functional measures in living tissue and anatomical measures in fixed tissue have played critical roles in advancing our understanding of the brain. However, making direct comparisons between different imaging modalities, particularly spanning living and fixed tissue, has remained challenging. For example, comparing brain-wide neural dynamics across experiments and aligning such data to anatomical resources, such as gene expression patterns or connectomes, requires precise alignment to a common set of anatomical coordinates. However, reaching this goal is difficult because registering in vivo functional imaging data to ex vivo reference atlases requires accommodating differences in imaging modality, microscope specification, and sample preparation. We overcome these challenges in Drosophila by building an in vivo reference atlas from multiphoton-imaged brains, called the Functional Drosophila Atlas. We then develop a registration pipeline, BrIdge For Registering Over Statistical Templates (BIFROST), for transforming neural imaging data into this common space and for importing ex vivo resources such as connectomes. Using genetically labeled cell types as ground truth, we demonstrate registration with a precision of less than 10 microns. Overall, BIFROST provides a pipeline for registering functional imaging datasets in the fly, both within and across experiments.

中文翻译：

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BIFROST：一种注册果蝇大脑的不同成像数据集的方法


跨越活组织功能测量和固定组织解剖测量的成像方法在促进我们对大脑的理解方面发挥了关键作用。然而，在不同的成像方式之间进行直接比较，特别是跨越活组织和固定组织，仍然具有挑战性。例如，比较不同实验的全脑神经动力学并将此类数据与解剖资源（如基因表达模式或连接组）对齐，需要与一组通用的解剖坐标精确对齐。然而，实现这一目标是困难的，因为将体内功能成像数据注册到离体参考图谱需要适应成像模式、显微镜规格和样品制备的差异。我们通过从多光子成像大脑构建体内参考图谱来克服果蝇中的这些挑战，称为功能性果蝇图谱。然后，我们开发了一个注册管道 BrIdge For Registering Over Statistical Templates （BIFROST），用于将神经成像数据转换为这个公共空间并导入体外资源，例如连接组。使用基因标记的细胞类型作为基本事实，我们展示了小于 10 微米的配准精度。总体而言，BIFROST 提供了一个管道，用于在实验内和实验之间动态注册功能成像数据集。