Bayesian reduced rank regression models generalizable neural fingerprints that differentiate between individuals in magnetoencephalography data

Joonas Haakana*, Susanne Merz, Samuel Kaski, Hanna Renvall, Riitta Salmelin

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Recent magnetoencephalography (MEG) studies have reported that functional connectivity (FC) and power spectra can be used as neural fingerprints in differentiating individuals. Such studies have mainly used correlations between measurement sessions to distinguish individuals from each other. However, it has remained unclear whether such correlations might reflect a more generalizable principle of individually distinctive brain patterns. Here, we evaluated a machine-learning based approach, termed latent-noise Bayesian reduced rank regression (BRRR) as a means of modelling individual differences in the resting-state MEG data of the Human Connectome Project (HCP), using FC and power spectra as neural features. First, we verified that BRRR could model and reproduce the differences between metrics that correlation-based fingerprinting yields. We trained BRRR models to distinguish individuals based on data from one measurement and used the models to identify subsequent measurement sessions of those same individuals. The best performing BRRR models, using only 20 spatiospectral components, were able to identify subjects across measurement sessions with over 90% accuracy, approaching the highest correlation-based accuracies. Using cross-validation, we then determined whether that BRRR model could generalize to unseen subjects, successfully classifying the measurement sessions of novel individuals with over 80% accuracy. The results demonstrate that individual neurofunctional differences can be reliably extracted from MEG data with a low-dimensional predictive model and that the model is able to classify novel subjects.

Original languageEnglish
Pages (from-to)2320-2335
Number of pages16
JournalEuropean Journal of Neuroscience
Volume59
Issue number9
Early online date14 Mar 2024
DOIs
Publication statusPublished - May 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • computational modelling
  • individual differences
  • machine learning
  • neural fingerprinting
  • resting-state

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