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Bayesian reduced rank regression reveals generalisable neural fingerprints that differentiate between individuals in magnetoencephalography data
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  • Joonas Haakana,
  • Susanne Merz,
  • Samuel Kaski,
  • Hanna Renvall,
  • Riitta Salmelin
Joonas Haakana
Aalto University

Corresponding Author:joonas.haakana@aalto.fi

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Susanne Merz
Aalto University
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Samuel Kaski
Aalto University
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Hanna Renvall
Aalto University
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Riitta Salmelin
Aalto University
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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 generalisable 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 measurements 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 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.
12 Sep 2023Submitted to European Journal of Neuroscience
12 Sep 2023Submission Checks Completed
12 Sep 2023Assigned to Editor
13 Sep 2023Review(s) Completed, Editorial Evaluation Pending
13 Sep 2023Reviewer(s) Assigned
19 Oct 2023Editorial Decision: Revise Major
05 Feb 2024Review(s) Completed, Editorial Evaluation Pending