Solving the where problem and quantifying geometric variation in neuroanatomy using generative diffeomorphic mapping.

ABSTRACT: A current focus in neuroscience is to map neuronal cell types in whole vertebrate brains using different imaging modalities. Mapping modern molecular and anatomical datasets into a common atlas includes challenges that existing workflows do not adequately address: multimodal signals, missing data or non reference signals, and quantification of individual variation. Our solution implements a generative model describing the likelihood of data given a sequence of transforms of an atlas, and a maximum a posteriori estimation framework. Our approach allows composition of mappings across chains of datasets rather than only pairs, and computes metrics for geometric quantification. We study a range of datasets (in/ex-vivo MRI, STP and fMOST, 2D serial histology, snRNAseq prepared tissue), quantify

SUBMITTER: Tward DJ 

PROVIDER: S-EPMC12645051 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature