Bayesian reduced rank regression models generalizable neural fingerprints that differentiate between individuals in magnetoencephalography data
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2024-05
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en
Pages
16
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European Journal of Neuroscience, Volume 59, issue 9, pp. 2320-2335
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.Description
Publisher Copyright: © 2024 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd. | openaire: EC/H2020/964220/EU//AI-Mind
Keywords
computational modelling, individual differences, machine learning, neural fingerprinting, resting-state
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Citation
Haakana, J, Merz, S, Kaski, S, Renvall, H & Salmelin, R 2024, ' Bayesian reduced rank regression models generalizable neural fingerprints that differentiate between individuals in magnetoencephalography data ', European Journal of Neuroscience, vol. 59, no. 9, pp. 2320-2335 . https://doi.org/10.1111/ejn.16292