Year: 2026
Type: preprint
Abstract: 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 variety of existing measures often produces conflicting results. To address this problem, we perform a systematic comparison of 16 established similarity measures. First, we show that different measures consistently disagree on which brain networks are most similar. Second, we rank all measures according to multiple criteria such as biological plausibility, computational efficiency, and sensitivity. We show that DeltaCon is the most accurate measure in terms of parameter recovery, and that it ranks among the fastest and most noise-tolerant. For this reason, we conclude that DeltaCon is the best general-purpose similarity measure. However, no measure ranks best on more than two of the five criteria we assessed, and different measures should be preferred depending on the exact research question. Measure selection can now be made on evidence and should be a crucial decision in any study that compares brain networks.
Institutions: Technical University of Munich, University of Cambridge
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: Brain Network Development Under Adversity, G2353581182, G2493601830, G2537968379, ANXIOMICS: Finding the mind, brain and immune pathways to anxiety, Computational modelling of synergistic information in artificial and biological neural networks, Computational modelling of synergistic information in artificial and biological neural networks, ANXIOMICS: Finding the mind, brain and immune pathways to anxiety, G8347284120
OpenAlex ID: W7214079902