# Structural Connectome Validation Using Pairwise Classification

## TL;DR

This study demonstrates that structural connectomes can be highly uniquely identified across individuals using pairwise classification, achieving near-perfect accuracy, which has implications for brain aging and disease diagnosis.

## Contribution

The paper introduces a pairwise classification method to validate the uniqueness of structural connectomes across individuals using large longitudinal datasets.

## Key findings

- Achieved 0.99 AUC in classifying connectome pairs from the same or different individuals.
- Features based on connectome weights and network measures are highly distinctive.
- Method may improve preprocessing for brain aging and early diagnosis studies.

## Abstract

In this work, we study the extent to which structural connectomes and topological derivative measures are unique to individual changes within human brains. To do so, we classify structural connectome pairs from two large longitudinal datasets as either belonging to the same individual or not. Our data is comprised of 227 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 226 from the Parkinson's Progression Markers Initiative (PPMI). We achieve 0.99 area under the ROC curve score for features which represent either weights or network structure of the connectomes (node degrees, PageRank and local efficiency). Our approach may be useful for eliminating noisy features as a preprocessing step in brain aging studies and early diagnosis classification problems.