# Exploring Heritability of Functional Brain Networks with Inexact Graph Matching

## TL;DR

This paper introduces a novel graph edit distance method for comparing individual brain networks, capturing their similarities and element correspondences despite variability, validated on twin data from the Human Connectome Project.

## Contribution

A new inexact graph matching technique based on graph edit distance for comparing functional brain networks while preserving element correspondences.

## Key findings

- Accurately reflects similarities between individual brain networks.
- Provides element correspondences despite variability.
- Validated on twin dataset from Human Connectome Project.

## Abstract

Data-driven brain parcellations aim to provide a more accurate representation of an individual's functional connectivity, since they are able to capture individual variability that arises due to development or disease. This renders comparisons between the emerging brain connectivity networks more challenging, since correspondences between their elements are not preserved. Unveiling these correspondences is of major importance to keep track of local functional connectivity changes. We propose a novel method based on graph edit distance for the comparison of brain graphs directly in their domain, that can accurately reflect similarities between individual networks while providing the network element correspondences. This method is validated on a dataset of 116 twin subjects provided by the Human Connectome Project.