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

## TL;DR

This paper introduces a new method to align and quantify brain imaging data from different sources, enabling better integration of neuroscience datasets.

## Contribution

The novel contribution is a generative diffeomorphic mapping approach that enables multi-modal dataset alignment and quantifies geometric variation.

## Key findings

- Individual variation in neuroanatomy is often greater than differences caused by tissue processing techniques.
- The method allows composition of mappings across chains of datasets and computes geometric quantification metrics.
- Open-source tools and dataset standards are provided to support large-scale integration of neuroimaging data.

## 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), quantifying cell density and geometric fluctuations across covariates, and reveal that individual variation is often greater than differences due to tissue processing techniques. We provide open source code, dataset standards, and a web interface. This establishes a quantitative workflow for unifying multi-modal whole-brain images in an atlas framework, validated using mouse datasets, enabling large scale integration of datasets essential to modern neuroscience.

Challenges in mapping modern molecular and anatomical datasets into a common atlas are not fully addressed. Here authors present approaches to aligning multimodal neuroimaging data and quantifying geometric variability. Authors also make sure open-source code, dataset standards, and a web interface are available, enabling large scale integration of datasets essential to modern neuroscience.