How Similar Are Two Brains? A Comprehensive Benchmark of Brain Network Similarity Measures
A central goal of neuroscience is to establish how similar (or different) brain networks are across individuals, development, psychiatric conditions, and even between biological and artificial brains. Whenever one such comparison is made, it relies on the assumption that we have a measure that produces an accurate and plausible similarity score. Yet, the field lacks one such measure, and the wide…
The objective of neuroscience is to determine the similarity or difference among brain networks across various factors. Currently, there is no single measure that accurately determines similarity, leading to conflicting results when researchers compare brain networks using different methods. To resolve this issue, a study was conducted to systematically evaluate 16 existing similarity measures.
Initially, the findings revealed that these different measures often disagree on which brain networks are most similar. To further analyze the effectiveness of these measures, they were ranked based on several criteria, including biological plausibility, computational efficiency, and sensitivity. The research shows that DeltaCon emerges as the most accurate measure when it comes to parameter recovery, and it also ranks among the fastest and most noise-tolerant options.
Given these results, the study concludes that DeltaCon is the best general-purpose similarity measure. However, it remains clear that no single measure is optimal for every situation, as each measure ranks lowest on at least one of the five assessed criteria. Therefore, the researchers recommend that measure selection should be based on the specific research question and the desired criteria, making it a crucial decision in any study comparing brain networks.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
