Brain-wide interpolation and conditioning of gene expression in the human brain using Implicit Neural Representations

TL;DR

This study applies Implicit Neural Representations to interpolate and generate detailed, voxel-level gene expression maps across the human brain, enhancing spatial transcriptomics analysis.

Contribution

It introduces a novel application of INR models for high-resolution spatial gene expression mapping in the human brain.

Findings

Successful interpolation of gene expression data at voxel resolution

Enhanced spatial maps compared to baseline methods

Potential for improved understanding of brain gene expression patterns

Abstract

In this paper, we study the efficacy and utility of recent advances in non-local, non-linear image interpolation and extrapolation algorithms, specifically, ideas based on Implicit Neural Representations (INR), as a tool for analysis of spatial transcriptomics data. We seek to utilize the microarray gene expression data sparsely sampled in the healthy human brain, and produce fully resolved spatial maps of any given gene across the whole brain at a voxel-level resolution. To do so, we first obtained the 100 top AD risk genes, whose baseline spatial transcriptional profiles were obtained from the Allen Human Brain Atlas (AHBA). We adapted Implicit Neural Representation models so that the pipeline can produce robust voxel-resolution quantitative maps of all genes. We present a variety of experiments using interpolations obtained from Abagen as a baseline/reference.