Coord2Region: A Python Package for Mapping 3D Brain Coordinates to Atlas Labels, Literature, and AI Summaries

TL;DR

Coord2Region is a Python package that automates mapping 3D brain coordinates to anatomical regions, links them to neuroimaging literature, and generates AI-based summaries to enhance neuroimaging research workflows.

Contribution

It introduces a comprehensive, open-source tool integrating coordinate mapping, literature linking, and AI summaries, streamlining neuroimaging analysis and interpretation workflows.

Findings

Supports batch and single-coordinate queries

Integrates with NiMARE, Neurosynth, NeuroQuery

Provides web interface and cloud execution

Abstract

We present Coord2Region, an open-source Python package that streamlines coordinate-based neuroimaging workflows by automatically mapping 3D brain coordinates (e.g., MNI or Talairach) to anatomical regions across multiple atlases. The package links mapped coordinates to meta-analytic resources via the Neuroimaging Meta-Analysis Research Environment (NiMARE) , providing direct integration with Neurosynth and NeuroQuery. This directly connects coordinates and regions to the broader neuroimaging literature. In addition to atlas-based labeling and literature retrieval, Coord2Region offers an optional large language model (LLM) functionality that generates text summaries of linked studies and illustrative images of queried regions. These AI-assisted features are intended to support interpretation and exploration, while remaining clearly complementary to peer-reviewed literature and established neuroimaging tools. Coord2Region provides a unified pipeline with a robust command-line interface, flexible dataset management, and provider-agnostic LLM utilities, and it supports both single-coordinate and high-throughput batch queries with nearest-region fallback for volume and surface atlases. Furthermore, Coord2Region includes a web interface for interactive configuration (via JSON Schema forms) and cloud execution (via Hugging Face), enabling users to build YAML configurations and run analyses in-browser without local installation. Together, these capabilities lower friction, reduce manual errors, and improve reproducibility in coordinate-centric neuroimaging workflows, promoting more robust and transparent research practices.