BIFROST: A method for registering diverse imaging datasets

The heterogeneity of brain imaging methods in neuroscience provides rich data that cannot be captured by a single technique, and our interpretations benefit from approaches that enable easy comparison both within and across different data types. 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, this is challenging 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 (FDA). We then develop a two-step 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.

神经科学领域内脑成像方法的异质性催生了单一技术无法囊括的丰富数据，而我们的解读将受益于能够轻松实现不同数据类型内部及跨类型间对比的方法。例如，在不同实验间比较全脑神经动态，并将此类数据与基因表达模式、连接组（connectome）等解剖学资源对齐，需要将其精准匹配至一套统一的解剖学坐标。然而，这一过程颇具挑战：将活体（in vivo）功能成像数据配准至离体（ex vivo）参考图谱时，需要适配成像模态、显微镜规格及样本制备方式之间的差异。我们以果蝇（Drosophila）为研究对象，通过对多光子成像的大脑构建活体参考图谱，命名为果蝇功能图谱（Functional Drosophila Atlas，FDA）。随后我们开发了一套两步式流程：用于统计模板配准的桥接工具（BrIdge For Registering Over Statistical Templates，BIFROST），可将神经成像数据转换至该统一空间，并导入连接组等离体资源。我们以基因标记的细胞类型作为真实基准（ground truth），证明该配准精度可达10微米以下。综上，BIFROST为果蝇体内跨实验及实验内的功能成像数据集配准提供了一套完整流程。