Using normative modeling and machine learning for detecting mild traumatic brain injury from magnetoencephalography data

New biomarkers are urgently needed for many brain disorders; for example, the diagnosis of mild traumatic brain injury (mTBI) is challenging as the clinical symptoms are diverse and nonspecific. EEG and MEG studies have demonstrated several population-level indicators of mTBI that could serve as objective markers of brain injury. However, deriving clinically useful biomarkers for mTBI and other brain disorders from EEG/MEG signals is hampered by the large inter-individual variability even across healthy people. Here, we used a multivariate machine-learning approach to detect mTBI from resting-state MEG measurements. To address the heterogeneity of the condition, we employed a normative modeling approach and modeled MEG signal features of individual mTBI patients as deviations with respect to the normal variation. To this end, a normative dataset comprising 621 healthy participants was used to determine the variation in power spectra across the cortex. In addition, we constructed normative datasets based on age-matched subsets of the full normative data. To discriminate patients from healthy control subjects, we trained support-vector-machine classifiers on the quantitative deviation maps for 25 mTBI patients and 20 controls not included in the normative dataset. The best performing classifier made use of the full normative data across the entire age and frequency ranges. This classifier was able to distinguish patients from controls with an accuracy of 79%. Inspection of the trained model revealed that low-frequency activity in the theta frequency band (4–8 Hz) is a significant indicator of mTBI, consistent with earlier studies. The results demonstrate the feasibility of using normative modeling of MEG data combined with machine learning to advance diagnosis of mTBI and identify patients that would benefit from treatment and rehabilitation. The current approach could be applied to a wide range of brain disorders, thus providing a basis for deriving MEG/ EEG-based biomarkers.

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Funding Information: This research was supported by the European Research Council grant #658578, European Commission Regional Development Fund REACT-EU (#309487) and Neurocenter Finland (Functional Brain Imaging Biobank pilot project) to LP. Support was awarded to ML by the Swedish Cultural Foundation in Finland, the Finnish Cultural Foundation, and the Sohlberg Foundation. HK was supported by the Paulo Foundation, the Finnish Medical Foundation, and Helsinki University Hospital Research Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank Hanna Kataja for automated pipelines for calculating cortical power spectra for large-scale MEG data. Publisher Copyright: Copyright: © 2023 Itälinna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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10.1371/journal.pcbi.1011613

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Itälinna, V, Kaltiainen, H, Forss, N, Liljeström, M & Parkkonen, L 2023, ' Using normative modeling and machine learning for detecting mild traumatic brain injury from magnetoencephalography data ', PLoS computational biology, vol. 19, no. 11, e1011613, pp. 1-19 . https://doi.org/10.1371/journal.pcbi.1011613

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https://urn.fi/URN:NBN:fi:aalto-202312117201

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[article-cris] Perustieteiden korkeakoulu / SCI

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Using normative modeling and machine learning for detecting mild traumatic brain injury from magnetoencephalography data

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