# BrainConnect: processing brain connectivity and spatial transcriptomics data for integrative analysis

## TL;DR

This paper introduces BrainConnect, a software tool that integrates brain connectivity and spatial transcriptomics data to predict and understand neural connections and their molecular basis.

## Contribution

The novel contribution is a software framework that processes brain connectivity and spatial transcriptomics data together to predict connectivity strengths using machine learning.

## Key findings

- The model accurately predicted connectivity strengths based on spatial transcriptomics data.
- The software helps identify important genes potentially involved in regulating brain connectivity.
- BrainConnect provides a consistent data format for integrative analysis of brain datasets.

## Abstract

Characterizing the neuronal connectomes provides route to understand the basis of neural circuit in brains, one of the central missions in neuroscience, but the mapped connectivity is absent of molecular information, obscuring the understanding on the important genes underlying the connectomes. The whole-brain spatial transcriptomics data provide the opportunity to predict and understand the brain connectivity. However, there is no method to process these datasets in consistent data format for integrative analysis.

In this work, we developed a software to process different kinds of mouse brain connectivity data together with spatial transcriptomics in consistent brain regions to define the connectivity path and strength and then used the long short-term memory network to predict connectivity strengths from the spatial transcriptomics by using our data framework. We evaluated the model in different ways, and the results showed that our model accurately predicted the connectivity strengths and helped in selecting the important genes potentially involved in the regulation, establishment or maintenance of brain connectivity.

The software is freely available at Github (https://github.com/CPenglab/BrainConnect) and Pypi (https://pypi.org/project/BrainConnect). An archived version is available at https://doi.org/10.5281/zenodo.18440094.