# Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification

## TL;DR

This study systematically compares 35 methods for constructing structural brain networks from diffusion MRI, evaluating their reliability and ability to distinguish individuals using pairwise classification across multiple datasets.

## Contribution

It introduces a comprehensive comparison of different connectome-building pipelines and assesses their effectiveness in individual identification.

## Key findings

- Certain pipelines achieve high classification accuracy.
- Connectome weights and topological measures are effective features.
- Comparison with ICC highlights strengths and limitations.

## Abstract

There is no consensus on how to construct structural brain networks from diffusion MRI. How variations in pre-processing steps affect network reliability and its ability to distinguish subjects remains opaque. In this work, we address this issue by comparing 35 structural connectome-building pipelines. We vary diffusion reconstruction models, tractography algorithms and parcellations. Next, we classify structural connectome pairs as either belonging to the same individual or not. Connectome weights and eight topological derivative measures form our feature set. For experiments, we use three test-retest datasets from the Consortium for Reliability and Reproducibility (CoRR) comprised of a total of 105 individuals. We also compare pairwise classification results to a commonly used parametric test-retest measure, Intraclass Correlation Coefficient (ICC).