Progress towards a cellularly resolved mouse mesoconnectome is empowered by data fusion and new neuroanatomy techniques

NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS

卷:128

Review

Timonidis, Nestor¹  Tiesinga, Paul H. E.¹ 

[1] Radboud Univ Nijmegen, Donders Ctr Neurosci, Neuroinformat Dept, Heyendaalseweg 135, NL-6525 AJ Nijmegen, Netherlands

关键词: Neuroanatomy review;    Data fusion;    Computational framework;    Cell-type specificity;    Mouse mesoconnectome;    Shared factorisation;    Multi-modal clustering;    Probabilistic inference;    Spatial registration;    Spatial transcriptomics and proteomics;    Connectomics;    Tract-tracing;    In situ hybridization;    Single-cell RNA sequencing;    Morphological reconstructions;    Barcode sequencing;    Light-sheet microscopy;    Diffusion tensor imaging;   

DOI  :  10.1016/j.neubiorev.2021.06.016

来源: Elsevier

【 摘 要 】

Over the past decade there has been a rapid improvement in techniques for obtaining large-scale cellular level data related to the mouse brain connectome. However, a detailed mapping of cell-type-specific projection patterns is lacking, which would, for instance, allow us to study the role of circuit motifs in cognitive processes. In this work, we review advanced neuroanatomical and data fusion techniques within the context of a proposed Multimodal Connectomic Integration Framework for augmenting the cellularly resolved mouse mesoconnectome. First, we emphasize the importance of registering data modalities to a common reference atlas. We then review a number of novel experimental techniques that can provide data for characterizing cell-types in the mouse brain. Furthermore, we examine a number of data integration strategies, which involve fine-grained cell-type classification, spatial inference of cell densities, latent variable models for the mesoconnectome and multi-modal factorisation. Finally, we discuss a number of use cases which depend on connectome augmentation techniques, such as model simulations of functional connectivity and generating mechanistic hypotheses for animal disease models.

【 授权许可】

Free